diff --git a/README.md b/README.md index 32583ef..93ba3ce 100644 --- a/README.md +++ b/README.md @@ -1,41 +1,41 @@ # Introducción al Adversarial Machine Learning -[Taller de Adversarial Machine Learning](./docs/Introducción_al_Adversarial_Machine_Learning.pdf) para las [XV Jornadas STIC CCN-CERT](https://www.ccn-cert.cni.es/xvjornadas). +[Charla de Adversarial Machine Learning](TBD) para [DragonJAR 2022](https://www.dragonjarcon.org).

- XV Jornadas STIC CCN-CERT + DragonJAR 2022

## ¿Qué es el Adversarial Machine Learning? -Rama del machine learning que trata de averiguar los ataques que puede sufrir un modelo en la presencia de un adversario malicioso y cómo protegerse de ellos. +El [Adversarial Machine Learning](https://en.wikipedia.org/wiki/Adversarial_machine_learning) es la rama del machine learning que trata de averiguar los ataques que puede sufrir un modelo en la presencia de un adversario malicioso y cómo protegerse de ellos. ### Taxonomía de ataques El Adversarial Machine Learning establece que existen 4 tipos de ataque que pueden sufrir los modelos de ML.

- Taxonomía + Taxonomía

-* Extracción (o robo) de modelo: permiten a un adversario robar los parámetros de un modelo de machine learning. +* **Extracción (o robo de modelos)**: permiten a un adversario robar los parámetros de un modelo de machine learning.

- Ataques de extracción + Ataques de extracción

-* Inversión: tienen como objetivo invertir el flujo de información de un modelo de machine learning. Permiten a un adversario tener un conocimiento del modelo que no pretendía ser compartido de forma explícita. +* **Inversión**: tienen como objetivo invertir el flujo de información de un modelo de machine learning. Permiten a un adversario tener un conocimiento del modelo que no pretendía ser compartido de forma explícita.

- Ataques de inversión + Ataques de inversión

-* Envenenamiento: buscan corromper el conjunto de entrenamiento haciendo que un modelo de machine learning reduzca su precisión. Pueden añadir puertas traseras en el modelo. +* **Envenenamiento**: buscan corromper el conjunto de entrenamiento haciendo que un modelo de machine learning reduzca su precisión. Pueden añadir puertas traseras en el modelo.

- Ataques de envenenamiento + Ataques de envenenamiento

-* Evasión: un adversario inserta una pequeña perturbación (en forma de ruido) en la entrada de un modelo de machine learning para que clasifique de forma incorrecta (ejemplo adversario). +* **Evasión**: un adversario inserta una pequeña perturbación (en forma de ruido) en la entrada de un modelo de machine learning para que clasifique de forma incorrecta (ejemplo adversario).

- Ataques de evasión + Ataques de evasión

## Herramientas empleadas @@ -43,19 +43,23 @@ El Adversarial Machine Learning establece que existen 4 tipos de ataque que pued * [Adversarial Robustness Toolkit (ART)](https://adversarial-robustness-toolbox.readthedocs.io/en/latest): es una librería opensource de Adversarial Machine Learning que permite comprobar la robustez de los modelos de machine learning. Está desarrollada en Python e implementa ataques y defensas de extracción, inversión, envenenamiento y evasión. ART soporta los frameworks más populares: Tensorflow, Keras, PyTorch, MxNet, ScikitLearn, entre muchos otros). Además, no está limitada al uso de modelos que emplean imágenes como entrada, sino que soporta otros tipos de datos como audio, vídeo, datos tabulares, etc.

- Logo de ART + Logo de ART

* [Counterfit](https://github.com/Azure/counterfit) es una CLI escrita en Python y desarrollada por Microsoft. Desarrollada para auditorías de seguridad sobre modelos de ML. Implementa algoritmos de evasión de caja negra. Se basa en los ataques de las herramientas ART y [TextAttack](https://github.com/QData/TextAttack).

- Logo de Counterfit + Logo de Counterfit

## Notebooks > Todos los notebooks se pueden ejecutar más rápidamente empleando una GPU. > Se recomienda el uso de [Colab](https://colab.research.google.com), que permite emplear GPUs de forma gratuita y no tener que instalar nada en el equipo. +> +>

+> Logo de Google Colab +>

La carpeta `notebooks` contiene 5 notebooks que cubren ataques de extracción, inversión, envenenamiento y evasión en ART. @@ -75,14 +79,23 @@ En el mismo directorio, se encuentra el fichero `counterfit.md`, que muestra có 2. Importar todos los notebooks de este repositorio usando la pestaña `GitHub`, copiando la url de este repositorio.

- Importar notebooks + Importar notebooks

3. Cambiar el entorno de ejecución a `GPU`. Se realiza desde el menú `Entorno de ejecución` > `Cambiar entorno de ejecución`. Esto acelera la ejecución de los notebooks.

- Cambiar entorno de ejecución a GPU + Cambiar entorno de ejecución a GPU

## Crédito -Los notebooks de inversión y envenenamiento se basan en los [ejemplos y notebooks proporcionados]() por ART. +Los notebooks de inversión y envenenamiento se basan en los [ejemplos](https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/examples) y [notebooks](https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/notebooks) proporcionados por ART. + +# Ediciones anteriores + +* [Taller de Adversarial Machine Learning](./presentations/CCN-CERT_2021_Introducción_al_Adversarial_Machine_Learning.pdf) para las [XV Jornadas STIC CCN-CERT](https://www.ccn-cert.cni.es/xvjornadas). + +

+ XV Jornadas STIC CCN-CERT +

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"GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "CLrkyRc6-rD5" - }, - "source": [ - "# Adversarial Robustness Toolkit\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JgG8sFK-sd1z" - }, - "source": [ - "* Documentación: https://adversarial-robustness-toolbox.readthedocs.io/en/latest/\n", - "* Código: https://github.com/Trusted-AI/adversarial-robustness-toolbox\n", - "* Ejemplos: https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/examples" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NuoZ5bhE-2_0" - }, - "source": [ - "## Instalación" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Zua3aciV-O5K", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "38dfc887-2b6a-48bc-8c81-4e2404e6408c" - }, - "source": [ - "!pip install adversarial-robustness-toolbox==1.8.1" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: adversarial-robustness-toolbox==1.8.1 in /usr/local/lib/python3.7/dist-packages (1.8.1)\n", - "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.4.1)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.15.0)\n", - "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.19.5)\n", - "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (57.4.0)\n", - "Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (0.22.2.post1)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (4.62.3)\n", - "Requirement already satisfied: numba>=0.53.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (0.54.1)\n", - "Requirement already satisfied: llvmlite<0.38,>=0.37.0rc1 in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.8.1) (0.37.0)\n", - "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.8.1) (1.1.0)\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1M8cmX6Y_MaU" - }, - "source": [ - "import keras\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from art.utils import load_mnist" - ], - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "3JSlG18GC1RT" - }, - "source": [ - "import tensorflow as tf\n", - "tf.compat.v1.disable_eager_execution()\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ], - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "6k-Q4bnG_SWG" - }, - "source": [ - "%matplotlib inline" - ], - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYWQUSRC_WLT" - }, - "source": [ - "## Cargar datos" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hq1wjOgu_a1U" - }, - "source": [ - "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()" - ], - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "YCASWXza_dK1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d05ab014-a7cc-4f07-e57f-ce1fa87e4e62" - }, - "source": [ - "print(\"x_train shape:\", x_train.shape)\n", - "print(\"y_train shape:\", y_train.shape)\n", - "print(\"x_test shape:\", x_test.shape)\n", - "print(\"y_test shape:\", y_test.shape)\n", - "\n", - "print(\"min_pixel_value:\", min_pixel_value)\n", - "print(\"min_pixel_value:\", max_pixel_value)" - ], - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "x_train shape: (60000, 28, 28, 1)\n", - "y_train shape: (60000, 10)\n", - "x_test shape: (10000, 28, 28, 1)\n", - "y_test shape: (10000, 10)\n", - "min_pixel_value: 0.0\n", - "min_pixel_value: 1.0\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "x7I2LY9T_k22", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 282 - }, - "outputId": "50dd161f-c006-4d44-c7ff-db33570ba8be" - }, - "source": [ - "sample = 12345\n", - "plt.imshow(x_train[sample].reshape((28, 28)), cmap='gray', interpolation='none')\n", - "print(np.argmax(y_train[sample]))" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "3\n" - ] - }, - { - "output_type": "display_data", - "data": { - "image/png": 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ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gGp89T6o_s3M" - }, - "source": [ - "## Entrenar modelo" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "gTOks8pDAObr" - }, - "source": [ - "from art.estimators.classification import KerasClassifier" - ], - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "IwvRBqtoARMM", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "620b5211-2956-4751-d577-c9e5302a1c9b" - }, - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(filters=4, kernel_size=(5, 5), strides=1, activation=\"relu\", input_shape=(28, 28, 1)))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Conv2D(filters=10, kernel_size=(5, 5), strides=1, activation=\"relu\", input_shape=(23, 23, 4)))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Flatten())\n", - "model.add(Dense(100, activation=\"relu\"))\n", - "model.add(Dense(10, activation=\"softmax\"))\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=[\"accuracy\"])\n", - "classifier = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)\n", - "\n", - "classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)" - ], - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 60000 samples\n", - "Epoch 1/3\n", - "60000/60000 [==============================] - 5s 83us/sample - loss: 0.1592 - accuracy: 0.9499\n", - "Epoch 2/3\n", - "60000/60000 [==============================] - 4s 65us/sample - loss: 0.0836 - accuracy: 0.9747\n", - "Epoch 3/3\n", - "60000/60000 [==============================] - 4s 64us/sample - loss: 0.0734 - accuracy: 0.9785\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MsMBfuNy0raY", - "outputId": "17cbcb8f-207d-4a30-f323-4896ad8a52e8" - }, - "source": [ - "predictions_test = classifier.predict(x_test)\n", - "accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test examples: {0:.2f}%\".format(accuracy * 100))" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test examples: 97.57%\n" - ] - } - ] - } - ] -} \ No newline at end of file +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Copia de art-install","provenance":[{"file_id":"https://github.com/jiep/adversarial-machine-learning/blob/main/notebooks/art_install.ipynb","timestamp":1660402179352}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"CLrkyRc6-rD5"},"source":["# Adversarial Robustness Toolkit (ART)\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"JgG8sFK-sd1z"},"source":["* Documentación: https://adversarial-robustness-toolbox.readthedocs.io/en/latest/\n","* Código: https://github.com/Trusted-AI/adversarial-robustness-toolbox\n","* Ejemplos: https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/examples"]},{"cell_type":"markdown","metadata":{"id":"NuoZ5bhE-2_0"},"source":["## Instalación"]},{"cell_type":"code","metadata":{"id":"Zua3aciV-O5K","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b807aa60-77b1-4c41-8ce2-e09485c849a6","executionInfo":{"status":"ok","timestamp":1660402065222,"user_tz":-120,"elapsed":6314,"user":{"displayName":"","userId":""}}},"source":["!pip install adversarial-robustness-toolbox==1.11.0"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting adversarial-robustness-toolbox==1.11.0\n"," Downloading adversarial_robustness_toolbox-1.11.0-py3-none-any.whl (1.3 MB)\n","\u001b[K |████████████████████████████████| 1.3 MB 33.6 MB/s \n","\u001b[?25hRequirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.7.3)\n","Requirement already satisfied: numba>=0.53.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (0.56.0)\n","Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.0.2)\n","Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.21.6)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (57.4.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.15.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (4.64.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.12.0)\n","Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (0.39.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (3.1.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (1.1.0)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.1.1)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (3.8.1)\n","Installing collected packages: adversarial-robustness-toolbox\n","Successfully installed adversarial-robustness-toolbox-1.11.0\n"]}]},{"cell_type":"code","metadata":{"id":"1M8cmX6Y_MaU"},"source":["import keras\n","from keras.models import Sequential\n","from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from art.utils import load_mnist"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3JSlG18GC1RT"},"source":["import tensorflow as tf\n","tf.compat.v1.disable_eager_execution()\n","\n","import warnings\n","warnings.filterwarnings('ignore')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6k-Q4bnG_SWG"},"source":["%matplotlib inline"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rYWQUSRC_WLT"},"source":["## Cargar datos"]},{"cell_type":"code","metadata":{"id":"hq1wjOgu_a1U"},"source":["(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YCASWXza_dK1","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e9c8c81d-f57e-440a-ff1f-fb92f70e69db","executionInfo":{"status":"ok","timestamp":1660402099444,"user_tz":-120,"elapsed":61,"user":{"displayName":"","userId":""}}},"source":["print(\"x_train shape:\", x_train.shape)\n","print(\"y_train shape:\", y_train.shape)\n","print(\"x_test shape:\", x_test.shape)\n","print(\"y_test shape:\", y_test.shape)\n","\n","print(\"min_pixel_value:\", min_pixel_value)\n","print(\"min_pixel_value:\", max_pixel_value)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x_train shape: (60000, 28, 28, 1)\n","y_train shape: (60000, 10)\n","x_test shape: (10000, 28, 28, 1)\n","y_test shape: (10000, 10)\n","min_pixel_value: 0.0\n","min_pixel_value: 1.0\n"]}]},{"cell_type":"code","metadata":{"id":"x7I2LY9T_k22","colab":{"base_uri":"https://localhost:8080/","height":282},"outputId":"47f85d4f-807b-49b5-fbd3-555dbfef82f7","executionInfo":{"status":"ok","timestamp":1660402116438,"user_tz":-120,"elapsed":1137,"user":{"displayName":"","userId":""}}},"source":["sample = 12345\n","plt.imshow(x_train[sample].reshape((28, 28)), cmap='gray', interpolation='none')\n","print(np.argmax(y_train[sample]))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["3\n"]},{"output_type":"display_data","data":{"text/plain":["
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JKNdcAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"gGp89T6o_s3M"},"source":["## Entrenar modelo"]},{"cell_type":"code","metadata":{"id":"gTOks8pDAObr"},"source":["from art.estimators.classification import KerasClassifier"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"IwvRBqtoARMM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"afc4136f-e2a4-46a1-d10b-3ccc68f24ac9","executionInfo":{"status":"ok","timestamp":1660402145987,"user_tz":-120,"elapsed":20478,"user":{"displayName":"","userId":""}}},"source":["model = Sequential()\n","model.add(Conv2D(filters=4, kernel_size=(5, 5), strides=1, activation=\"relu\", input_shape=(28, 28, 1)))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Conv2D(filters=10, kernel_size=(5, 5), strides=1, activation=\"relu\", input_shape=(23, 23, 4)))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Flatten())\n","model.add(Dense(100, activation=\"relu\"))\n","model.add(Dense(10, activation=\"softmax\"))\n","\n","model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=[\"accuracy\"])\n","classifier = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)\n","\n","classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train on 60000 samples\n","Epoch 1/3\n","60000/60000 [==============================] - 11s 189us/sample - loss: 0.1616 - accuracy: 0.9492\n","Epoch 2/3\n","60000/60000 [==============================] - 2s 41us/sample - loss: 0.0801 - accuracy: 0.9762\n","Epoch 3/3\n","60000/60000 [==============================] - 2s 41us/sample - loss: 0.0724 - accuracy: 0.9787\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MsMBfuNy0raY","outputId":"0a29857f-f2a9-4bb2-fd2d-90ec4d9d463a","executionInfo":{"status":"ok","timestamp":1660402151240,"user_tz":-120,"elapsed":754,"user":{"displayName":"","userId":""}}},"source":["predictions_test = classifier.predict(x_test)\n","accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test examples: {0:.2f}%\".format(accuracy * 100))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test examples: 97.61%\n"]}]}]} \ No newline at end of file diff --git a/notebooks/evasion.ipynb b/notebooks/evasion.ipynb index ff0e9b9..8a1a650 100644 --- a/notebooks/evasion.ipynb +++ b/notebooks/evasion.ipynb @@ -1,1432 +1 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "evasion", - "provenance": [], - "collapsed_sections": [], - 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https://adversarial-robustness-toolbox.readthedocs.io/en/latest/\n", - "* Código: https://github.com/Trusted-AI/adversarial-robustness-toolbox\n", - "* Ejemplos: https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/examples" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NuoZ5bhE-2_0" - }, - "source": [ - "## Instalación" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Zua3aciV-O5K", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "eb773691-42af-49c0-a363-f69f9c5f5c1c" - }, - "source": [ - "!pip install adversarial-robustness-toolbox==1.8.1" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: adversarial-robustness-toolbox==1.8.1 in /usr/local/lib/python3.7/dist-packages (1.8.1)\n", - "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.19.5)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.15.0)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (4.62.3)\n", - "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (57.4.0)\n", - "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.4.1)\n", - "Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.0.1)\n", - "Requirement already satisfied: numba>=0.53.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (0.54.1)\n", - "Requirement already satisfied: llvmlite<0.38,>=0.37.0rc1 in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.8.1) (0.37.0)\n", - "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.8.1) (1.1.0)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.8.1) (3.0.0)\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1M8cmX6Y_MaU" - }, - "source": [ - "import keras\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from art.utils import load_mnist" - ], - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "3JSlG18GC1RT" - }, - "source": [ - "import tensorflow as tf\n", - "tf.compat.v1.disable_eager_execution()\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ], - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "6k-Q4bnG_SWG" - }, - "source": [ - "%matplotlib inline" - ], - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYWQUSRC_WLT" - }, - "source": [ - "## Cargar datos" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hq1wjOgu_a1U" - }, - "source": [ - "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()" - ], - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gGp89T6o_s3M" - }, - "source": [ - "## Entrenar modelo" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "gTOks8pDAObr" - }, - "source": [ - "from art.estimators.classification import KerasClassifier" - ], - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "IwvRBqtoARMM", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ef99f5f9-7668-4fe1-d448-074aa99fa96e" - }, - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(filters=4, kernel_size=(5, 5), strides=1, activation=\"relu\", input_shape=(28, 28, 1)))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Conv2D(filters=10, kernel_size=(5, 5), strides=1, activation=\"relu\", input_shape=(23, 23, 4)))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Flatten())\n", - "model.add(Dense(100, activation=\"relu\"))\n", - "model.add(Dense(10, activation=\"softmax\"))\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=[\"accuracy\"])\n", - "classifier = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)\n", - "\n", - "classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 60000 samples\n", - "Epoch 1/3\n", - "60000/60000 [==============================] - 8s 141us/sample - loss: 0.1578 - accuracy: 0.9509\n", - "Epoch 2/3\n", - "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0844 - accuracy: 0.9747\n", - "Epoch 3/3\n", - "60000/60000 [==============================] - 4s 66us/sample - loss: 0.0787 - accuracy: 0.9766\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "HoS7HK-8ATSa", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a37219ef-6d41-4756-880b-f68e8aa7ba4a" - }, - "source": [ - "predictions_test = classifier.predict(x_test)\n", - "accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test examples: {0:.2f}%\".format(accuracy * 100))" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test examples: 97.89%\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8RjSGz4MAa5n" - }, - "source": [ - "# Ataques de evasión" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SR-gnZi-Jp0S" - }, - "source": [ - "## Generar ejemplos adversarios" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "LzFU47WnAd9T" - }, - "source": [ - "from art.attacks.evasion import FastGradientMethod, SaliencyMapMethod, CarliniL2Method" - ], - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "QH6qevlrAkGh" - }, - "source": [ - "attack_fgm = FastGradientMethod(estimator = classifier, eps = 0.2)\n", - "x_test_fgm = attack_fgm.generate(x=x_test)" - ], - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "awFmtipPFFla", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "14672626-64de-4d74-ea3c-301225c3aa13" - }, - "source": [ - "predictions_test = classifier.predict(x_test_fgm)\n", - "accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test adversarial examples: {0:.2f}%\".format(accuracy * 100))" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test adversarial examples: 34.70%\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "THJtvETx17eH" - }, - "source": [ - "## Ataque no dirigido" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "gYmcNjfG8Ypr", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d1574fe7-c1e4-4df9-d54f-f38a97b65375" - }, - "source": [ - "# Ataque no dirigido\n", - "best = (100, 0, None, None) # (acc, eps, x_test_fgm, predictions)\n", - "for eps in np.arange(0.0, 1.0, 0.1):\n", - " attack_fgm = FastGradientMethod(estimator = classifier, eps = eps)\n", - " x_test_fgm = attack_fgm.generate(x=x_test)\n", - " predictions_test = classifier.predict(x_test_fgm)\n", - " accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - " if(accuracy < best[0]):\n", - " best = (accuracy, eps, x_test_fgm, predictions_test)\n", - " print(\"Accuracy on test adversarial examples: {:.2f}% (eps={:.2f})\".format(accuracy * 100, eps))\n", - "print(\"Best results: accuracy: {:.2f}% eps={:.2f}\".format(best[0] * 100, best[1]))" - ], - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test adversarial examples: 97.89% (eps=0.00)\n", - "Accuracy on test adversarial examples: 69.01% (eps=0.10)\n", - "Accuracy on test adversarial examples: 34.70% (eps=0.20)\n", - "Accuracy on test adversarial examples: 21.60% (eps=0.30)\n", - "Accuracy on test adversarial examples: 17.11% (eps=0.40)\n", - "Accuracy on test adversarial examples: 15.09% (eps=0.50)\n", - "Accuracy on test adversarial examples: 14.67% (eps=0.60)\n", - "Accuracy on test adversarial examples: 15.49% (eps=0.70)\n", - "Accuracy on test adversarial examples: 17.37% (eps=0.80)\n", - "Accuracy on test adversarial examples: 18.59% (eps=0.90)\n", - "Best results: accuracy: 14.67% eps=0.60\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "noyhMckN1_JK" - }, - "source": [ - "## Ataque dirigido" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "O3Lnxy3W7fqe" - }, - "source": [ - "from art.utils import to_categorical" - ], - "execution_count": 13, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "ZWISeIXx-0sy", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "f0831f85-c64e-41f2-a763-adc1e74ede86" - }, - "source": [ - "# Ataque dirigido\n", - "y = np.ones(len(y_test))*9\n", - "y_targeted = to_categorical(y, nb_classes=10)\n", - "best_targeted = (0, 0.1, None, None) # (acc, eps, x_test_fgm, predictions)\n", - "for eps in np.arange(0.1, 1.0, 0.1):\n", - " attack_fgm = FastGradientMethod(estimator = classifier, eps = eps, targeted=True)\n", - " x_test_fgm = attack_fgm.generate(x=x_test, y=y_targeted)\n", - " predictions_test = classifier.predict(x_test_fgm)\n", - " accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_targeted, axis=1)) / len(y_targeted)\n", - " if(accuracy > best_targeted[0]):\n", - " best_targeted = (accuracy, eps, x_test_fgm, predictions_test)\n", - " print(\"Accuracy on test adversarial examples: {:.2f}% (eps={:.2f})\".format(accuracy * 100, eps))\n", - "print(\"Best results (targeted): accuracy: {:.2f}% eps={:.2f}\".format(best_targeted[0] * 100, best_targeted[1]))" - ], - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test adversarial examples: 16.97% (eps=0.10)\n", - "Accuracy on test adversarial examples: 28.28% (eps=0.20)\n", - "Accuracy on test adversarial examples: 27.38% (eps=0.30)\n", - "Accuracy on test adversarial examples: 23.55% (eps=0.40)\n", - "Accuracy on test adversarial examples: 19.48% (eps=0.50)\n", - "Accuracy on test adversarial examples: 16.09% (eps=0.60)\n", - "Accuracy on test adversarial examples: 13.51% (eps=0.70)\n", - "Accuracy on test adversarial examples: 11.33% (eps=0.80)\n", - "Accuracy on test adversarial examples: 9.99% (eps=0.90)\n", - "Best results (targeted): accuracy: 28.28% eps=0.20\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "udwfxbJoE9hM", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 290 - }, - "outputId": "c7673359-f518-43ff-8c8f-8e6eb0dfcc5a" - }, - "source": [ - "sample = 1234\n", - "fig = plt.figure(figsize=(20,10))\n", - "ax = fig.add_subplot(1, 4, 1)\n", - "ax.imshow(x_test[sample].reshape((28, 28)), cmap='gray', interpolation='none')\n", - "ax.set_title(\"Original: {}\".format(np.argmax(y_test[sample])))\n", - "ax.axis('off')\n", - "ax = fig.add_subplot(1, 4, 2)\n", - "ax.imshow(best[2][sample].reshape((28, 28)), cmap='gray', interpolation='none')\n", - "ax.set_title(\"Adversarial (FGSM): {}\".format(np.argmax(best[3][sample])))\n", - "ax.axis('off')\n", - "ax = fig.add_subplot(1, 4, 3)\n", - "ax.imshow(best_targeted[2][sample].reshape((28, 28)), cmap='gray', interpolation='none')\n", - "ax.set_title(\"Adversarial (FGSM - targeted): {}\".format(np.argmax(best_targeted[3][sample])))\n", - "ax.axis('off')\n", - "fig.show()" - ], - "execution_count": 31, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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AAJIojgAAAJLocwQA99OnV9Ik0bcJ1kx9eiVNkjWlb5MzRwAAAFEcAQAAJFEcAQAAJFEcAQAAJFEcAQAAJFEcAQAAJFEcAQAAJFEcAQAAJNEEFoAJMJONV1e3Jq/AzJnJxqurW5PX2cKZIwAAgCiOAAAAkiiOAAAAkiiOAAAAkiiOAAAAkiiOAAAAkiiOAAAAkiiOAAAAkmgCy0COO+64ZsyWW245dnyPPfZoznHllVc2Yz72sY+NHT/jjDOac9x8883NGFgTtBqiHnLIIdOeYya96U1vasYsXrx4BlaS/O53vxs7vs466zTnmKTHFlalmWqYetlllzVj+qxlp512asb81V/91djxTTfdtDlHH5/61KfGjp955pnNOWbTfpEzRwAAAFEcAQAAJFEcAQAAJFEcAQAAJFEcAQAAJFEcAQAAJFEcAQAAJElKrXX5g6Usf5A1whvf+MZmzFve8pZmzHbbbdeMKaX0WtPKdvrppzdj9t133xlYycyptU7Gg/8gyUUP1Kf/TZ8eRTOltd4FCxY059h1112bMdtuu20zppWL+vRtmylD5M1Jeq3IRWuePj1/hugd1GeOITzrWc9qxhx66KHNmCFyUZ9eSQsXLmzGtOa59tprm3MMsV80U89hH+NykTNHAAAAURwBAAAkURwBAAAkURwBAAAkURwBAAAkURwBAAAkURwBAAAkURwBAAAkSR6yqhfAsj30oQ9txrz73e9uxrzyla8cO/7kJz+5OcePf/zjZkyfRoWXXnrp2PH11luvOcf222/fjGk577zzpj0HrA4mqfnn5z73ubHjF154YXOOxYsXN2OOP/74ZszTn/70seO77LJLc44+uajVTHZSGmPDyjZUo9iWF7/4xc2Y1n7R6173ummvI+nXnLW1X/TDH/6wOUefXNRay+WXX96cYzZx5ggAACCKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSawK4yW2211djxo48+ujlHn2ZnLa0mhUly2GGHNWP6NJO94YYbxo6fcMIJzTn6NDu76667xo5/61vfas4Bs8XXvva1seMvfOELB9nO/Pnzx473ad76+c9/vhlz8cUXN2NaDQ/nzJnTnOOCCy5oxrz2ta9txgD9fOADH2jGfPCDH5z2dlqNWZN++0Vz585txixatGjs+Lx585pz9Nkvuvvuu8eOL1iwoDlHn0a+awpnjgAAAKI4AgAASKI4AgAASKI4AgAASKI4AgAASKI4AgAASKI4AgAASJKUWuvyB0tZ/iDLte666zZjWv06Xv3qVw+1nLH222+/ZsyXv/zlZsz666/fjDniiCPGjrf6oCTtXklJ8uY3v3ns+EUXXdSco9UTYHVTay2reg3TMRtz0bHHHjvtOfrkooMOOmjseJ/35RD69Dnq46STTmrGHHDAAWPHx/1dXBGlrNZvu5VCLlr9tPrbXHbZZYNs5x3veMfY8Y9+9KODbKdl6623bsZsvPHGzZgh9ov65KLrr7++GfOWt7xl7Phtt93WnGOm+hwN9XpqGZeLnDkCAACI4ggAACCJ4ggAACCJ4ggAACCJ4ggAACCJ4ggAACCJ4ggAACCJ4ggAACCJJrArxTbbbNOM+dGPfjTt7fRpDvaTn/xk7PjcuXObc+ywww7NmFNPPbUZ86hHPWrs+GGHHdacY6212vX8Kaec0oyZbTRenJ3OPffcZkzrvXvccccNtZyxhmqYOkQD1zvuuGOAlSQbbLDBIPOsSeQilueSSy4ZO77TTjsNsp0vfelLY8f333//5hyve93rmjF/8id/0oxp7RddddVVzTn65M63v/3tzZgWTWABAABmGcURAABAFEcAAABJFEcAAABJFEcAAABJFEcAAABJFEcAAABJFEcAAABJkoes6gWsiebMmdOM+e1vfzt2/N57723O8apXvaoZc/bZZ48d79Ow9pxzzmnGPOYxj2nGtJpJ9mkkC5Pu2GOPHWSeQw45ZNpzDJGLDjjggOYcH/rQh5oxZ511VjOm5aUvfWkzZv78+c2YVi764he/2HtNk2CI19wQrzcmy1BNO4doyrn++us3Y4Zo8vrYxz62GbPFFluMHd93332bc5xwwgnNmIULFzZjWg1c3//+9zfnmCQz1cB1ZXPmCAAAIIojAACAJIojAACAJIojAACAJIojAACAJIojAACAJIojAACAJPocrRTnn39+M+aKK64YO77jjjs259hss82aMa15TjrppOYcG2+8cTPmlFNOacYcc8wxzRigM0TvmpnKRVtuuWUz5uKLLx47XmttztHHeuut14w5/vjjx44P1asKZoOh+iktXrx47Pgll1zSnON973tfM+byyy8fO97KD3312S/6xCc+MXZ8qMd2CGtKD6M+nDkCAACI4ggAACCJ4ggAACCJ4ggAACCJ4ggAACCJ4ggAACCJ4ggAACCJ4ggAACBJUsY13iulDNOVjwd4/vOfP3a8T/OwPk1gW0opzZj3vOc9zZgPf/jD014LK0+ttf1ET7A1LRcN0WT0kEMOGWQ7rVx0+OGHN+cYIhcdd9xxzZg++YoH6vM66PN6GoJcNHMmqYFoH61cdOSRRw6ynYULF44dH2q/6JZbbmnGzKbGqpNmXC5y5ggAACCKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCTJQ1b1AmarRYsWjR2/9dZbm3MM0Xixj5///Oczsh2YLWaq4WYfrVx0xhlnNOeYP3/+tNexYMGCac8xkyapsWrLpKyDmTVUg9GZaibbykV9tBq8DqXPWn/wgx/MwEpYGZw5AgAAiOIIAAAgieIIAAAgieIIAAAgieIIAAAgieIIAAAgieIIAAAgSVJqrcsfLGX5g7PUIx/5yGbM85///GbMkUceOXZ88eLFzTm+/vWvN2P22WefseNz5sxpzjHuNbLEe9/73mbMl7/85bHj11xzTXMOHpxaa1nVa5iOScpFrf42Q/WUaW2nTy7af//9mzGtfNUnF33rW99qxrzkJS8ZOz5ULvrsZz/bjDnvvPPGju++++7NOYboc9RnjiFMUp8juWg4rf5DM9XnaKj9oic96Uljx++5557mHNtss00zZt68ec2YlksvvbQZ02e/aOONNx47PtR+Ueu1MFO9rIZ6TQ5hXC5y5ggAACCKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSKIwAAgCSawD7Ak5/85LHjfZr2Pfe5z532Or761a82Y17xilc0Y9Zee+2x45/85Cebc+y1117NmM0337wZc+ONN44d/8AHPtCc47jjjmvG8EAaL/YzRGPPobbTykW77bbbtNeRJPPnzx873uc99/d///fNmFYuOvjgg5tzzJQ+zWZXp1ykCexwJmm/aIgmsH2af86dO3fs+LOe9azmHDO1X3T22Wc3Y9Zaa/x5gac97WnNOfrk8IULFzZjWvtF119/fXOO1SkXaQILAACwGlEcAQAARHEEAACQRHEEAACQRHEEAACQRHEEAACQRHEEAACQRHEEAACQZJY1gd15552bMWeeeebY8Y022miQtfzmN78ZO/685z2vOcell146yFpatt5662bMu971rmbMHnvsMXb8cY97XHOOr3/9682Yfffdd+z47bff3pxjTaPx4szp0xywTy566lOfOna81by1r1YDwe9973vNOSYpF7XyTJIsXrx4iOU0nXrqqWPH++SiPg1cW685TWCHM1O5qE9z1iH0yUVHH3302PE+zU772GmnncaOt5q3JskznvGMQdbS0icXbbLJJs2YIfaLnvjEJzZjhtgvGqKpsCawAAAAqxHFEQAAQBRHAAAASRRHAAAASRRHAAAASRRHAAAASRRHAAAASRRHAAAASWZZE9gLLrigGTNv3rxpb+fXv/51M+YNb3jD2PFW88DV0VFHHTV2/G1ve9sg2/n0pz89dvzQQw8dZDurE40Xh9OnyWvLggULmjFDNHntk4ta75c+uWiIJqN9HteZamY67u/ikD772c8OMs8kNXltkYuGM0Sj2D77RVdcccW0tzPEftGPf/zjaa+jj6Ea8PZpePqNb3xj7Pj6668/yFo+85nPjB0f4jlOJqvJa4smsAAAAA2KIwAAgCiOAAAAkiiOAAAAkiiOAAAAkiiOAAAAkiiOAAAAkqxBfY523XXXZsx5553XjFl77bXHjvf5rv4999yzGfOd73ynGbOmmTt37tjx5z3vec05PvrRjzZjWn0Bdtlll+Ycl156aTNmdaK3yMz54Q9/2Iw58sgjmzFD5KKTTz65GTMphugflfTr+dPqLdInF+23337NmFYueutb39qcY+HChc0YfY5mzuqUi9Zbb71mzLnnntuMaeWinXbaaZC13H333WPH+/QfGqLPzkz2Odp///3HjvfJRdtss00zppWLvvCFLzTn6JOL9DkCAABYgyiOAAAAojgCAABIojgCAABIojgCAABIojgCAABIojgCAABIojgCAABIkjxkVS9gdXPNNdc0Y2Zjg9c+brnllrHjT3jCE5pzbLjhhs2YVoPHq666qjkHq59WE9GhGmUO0az0hBNOaMbMnz9/7HifXDQb9Xl+WrlorbXaxw1PO+20ZkyrOeOJJ57YnGOo5rjMnFYT0UlqlDlv3rwZ2c7222/fjGk9LjP1uPXZTp9GsX1iJmW/qE/z8j6GaKA7Ce8PZ44AAACiOAIAAEiiOAIAAEiiOAIAAEiiOAIAAEiiOAIAAEiiOAIAAEiiOAIAAEiyBjWB7dN49cYbb2zGzJkzZ+z4pptu2pyjT8xNN93UjJkUD3vYw5oxe+yxRzPm4x//+NjxrbbaqjnHDTfc0Iw5/PDDx47feeedzTmYnYZouNmn2eFee+3VjDnrrLOmvZavfOUrzZhJyUV9mvR+/vOfb8YcdNBBzZgXvOAFY8f32Wef5hx77713M+baa69txsyEPq/roZokM4whmmlOkj77RavTfe6zX3Tdddc1Y975zneOHd9kk02acyxatKgZc/XVV48dt190f84cAQAARHEEAACQRHEEAACQRHEEAACQRHEEAACQRHEEAACQRHEEAACQZA3qc9THq1/96mZMqy/IYx/72OYcrR4aSXL66aePHb/nnnuac/Sx7rrrjh1/0pOe1JzjC1/4QjNmhx12aMa07tMZZ5zRnONtb3tbM6ZPbwFmnyF6GA3lIx/5yLTnmD9/fjPm+OOPn/Z2hnLiiSeOHb/wwgubczznOc9pxhx44IF9l7Rcm222WTPmF7/4RTOm9ZobqrfQJL22aRuqn89ll1027e0ceuihzZg999xz7Hif90ufnl/XXHNNM2YIO++889jxuXPnNufYZZddmjFD7Bf12ed89rOf3YxpmanX5OrCmSMAAIAojgAAAJIojgAAAJIojgAAAJIojgAAAJIojgAAAJIojgAAAJIojgAAAJIkpda6/MFSlj+4htpwww3Hjl9xxRXNObbYYotmzMUXXzx2/LbbbmvO0UerOVufJmV33nlnM+acc85pxhx11FFjx1uPCQ9erbWs6jVMxxC5aHVrlNnKRa2mqkm/XHTRRReNHb/99tubc/TRas68YMGC5hx33XVXM+bkk09uxnz/+98fO77jjjs25+hjdXrNDdWQtkUuGq7h5hCGaNr5la98pRmzOu0Xvf71r2/Ocf755zdjzj777GbMr371q7Hjl1xySXOONaXx6kwbl4ucOQIAAIjiCAAAIIniCAAAIIniCAAAIIniCAAAIIniCAAAIIniCAAAIIniCAAAIIkmsCts3rx5zZhDDz20GbPtttuOHb/55punPUeSfPvb3x47fv311zfnePe7392MWbx4cTOGVUfjxdVPq4Fon1z0tKc9bajlTNsGG2wwdvyOO+4YZDt9Gq/OVMPTSTFJj4lctPppNa3tk4vWWWedZsx22203dvyVr3xlc45NNtmkGfP2t7997Pjee+/dnGP33XdvxgxhqIbBGsU+kCawAAAADYojAACAKI4AAACSKI4AAACSKI4AAACSKI4AAACSKI4AAACS6HMEs4LeIv3MVD+YIbbTZ45JMsT9GaoXT2tbk7SdIZ7nSerrJBf1M3kN4NMAAAFqSURBVFR/Gx6o1fOnz2M/VN+gSXme+9yfIdY6Sf2W9DkCAABoUBwBAABEcQQAAJBEcQQAAJBEcQQAAJBEcQQAAJBEcQQAAJBEcQQAAJBEE1iYFTRe5MFa05rNJqtXE9g1jVw0cyalwSgP3kw1m52k5qwzRRNYAACABsURAABAFEcAAABJFEcAAABJFEcAAABJFEcAAABJFEcAAABJFEcAAABJNIGFWUHjxZnTp2nqEI1K+5ikhqirk6Ea387Gx65FLposfRrFDtEgdKa2s6YZqpGvx/aBNIEFAABoUBwBAABEcQQAAJBEcQQAAJBEcQQAAJBEcQQAAJBEcQQAAJBEnyOYFfQWASaBXARMAn2OAAAAGhRHAAAAURwBAAAkURwBAAAkURwBAAAkURwBAAAkURwBAAAkURwBAAAkaTSBBQAAmC2cOQIAAIjiCAAAIIniCAAAIIniCAAAIIniCAAAIIniCAAAIEny/wGZasxifSzxugAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "O_GYhKOCB6SS" - }, - "source": [ - "## Otros métodos de ataque" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9w5pLUQrAo4e" - }, - "source": [ - "# Más métodos se pueden encontrar en \n", - "# https://adversarial-robustness-toolbox.readthedocs.io/en/latest/modules/attacks/evasion.html\n", - "\n", - "# Estos métodos tardan bastante más tiempo en ejecutar.\n", - "\n", - "# JSMA\n", - "# attack_jsma = SaliencyMapMethod(classifier = classifier, theta = 0.1)\n", - "# x_test_jsma = attack_jsma.generate(x=x_test)\n", - "\n", - "# Carlini&Wagner\n", - "# attack_cw2 = CarliniL2Method(classifier = classifier)\n", - "# x_test_cw2 = attack_cw2.generate(x=x_test)" - ], - "execution_count": 16, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bXZyqv27Ag9N" - }, - "source": [ - "## Entrenamiento adversario" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "lmH04l3D3wlr" - }, - "source": [ - "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()" - ], - "execution_count": 17, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "hZtOAlI5BGX2" - }, - "source": [ - "attack_fgm = FastGradientMethod(estimator = classifier, eps = 0.6)\n", - "x_train_fgm = attack_fgm.generate(x=x_train)\n", - "x_test_fgm = attack_fgm.generate(x=x_test)" - ], - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "BggbW6KoBJNk" - }, - "source": [ - "x_train = np.append(x_train, x_train_fgm, axis=0)\n", - "y_train = np.append(y_train, y_train, axis=0)" - ], - "execution_count": 19, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "IOdjNpvJBLIz", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "22324512-f722-4566-dcb3-e2898acfd2f0" - }, - "source": [ - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=[\"accuracy\"])\n", - "\n", - "classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)" - ], - "execution_count": 20, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 120000 samples\n", - "Epoch 1/3\n", - "120000/120000 [==============================] - 8s 70us/sample - loss: 0.3036 - accuracy: 0.9021\n", - "Epoch 2/3\n", - "120000/120000 [==============================] - 8s 69us/sample - loss: 0.2044 - accuracy: 0.9355\n", - "Epoch 3/3\n", - "120000/120000 [==============================] - 8s 68us/sample - loss: 0.1923 - accuracy: 0.9404\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "2WLDCQoIBRiT", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "cab5db56-a7af-40bd-dcfc-d537c2b44423" - }, - "source": [ - "predictions_test = classifier.predict(x_test)\n", - "accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test examples: {0:.2f}%\".format(accuracy * 100))\n", - "\n", - "predictions_fsm = classifier.predict(x_test_fgm)\n", - "accuracy = np.sum(np.argmax(predictions_fsm, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on adversarial test examples for FSGM attack: {0:.2f}%\".format(accuracy * 100))" - ], - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test examples: 96.49%\n", - "Accuracy on adversarial test examples for FSGM attack: 88.93%\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FaoK60dJcHcy" - }, - "source": [ - "## Entrenamiento adversario de forma nativa" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "brtrAWaCUSmz" - }, - "source": [ - "from art.defences.trainer import AdversarialTrainer" - ], - "execution_count": 22, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "AE_5ZSKLUuCu" - }, - "source": [ - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=[\"accuracy\"])" - ], - "execution_count": 23, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "7zL6s6qfUZ4Y" - }, - "source": [ - "defence = AdversarialTrainer(classifier=classifier, attacks=attack_fgm, ratio=0.6)" - ], - "execution_count": 24, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "TXRJQTipVFuu" - }, - "source": [ - "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()" - ], - "execution_count": 25, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "ww6WB1BjU59K", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81, - "referenced_widgets": [ - "1de560444d484f3eaab695a34b2f6a6b", - "4108c6d12b994efaaa7a8790069d63f7", - "9a18793195e34ba0bb457774402544ee", - "27de83d71c9f4a808da44be4ba7cb710", - "1611b759fcf243edbc25c36d1b66d5c2", - "3f73f6b212ec4c09908f3335e04f819b", - "7b5e70a0ee1645699683d2dfe9bac4b0", - "5b47f317b5ce4bc09b26653b9e5de0b7", - "513b5a2e127e480fa304d98990a57b83", - "c7d6b166939d42138bbbdc5ad7a02f82", - "3824196fd2184a158ab6d48ccceed2db", - "d0d7c36f39ab48f6a33075634b4109b1", - "85eee7953ce741dba04fb342b815e49f", - "8e8c66d0aed64908be123a40d9e7bf1a", - "932ebd0f8e1d4cbeb60bdc51bbd4d12f", - "2f82816aa6cb47d98278f30e42249880", - "dbb6e1236a1c40bfb534fb2743c1b0e6", - "9e1798eaac3041c2925083b5a0507bd0", - "404166b86a444cb4877a7618900a2c0e", - "485ff8e8c6f1423688e16b66de1f01b2", - "a4646120246849bf873d9d32f12ba686", - "4c9492e7c7ed410bba4fc7dc3a697a32" - ] - }, - "outputId": "bf47281b-a8f3-4817-f130-645ca6e53e74" - }, - "source": [ - "defence.fit(x=x_train, y=y_train, nb_epochs=3)" - ], - "execution_count": 26, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1de560444d484f3eaab695a34b2f6a6b", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Precompute adv samples: 0%| | 0/1 [00:00=0.53.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (0.56.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.15.0)\n","Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.0.2)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (4.64.0)\n","Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.21.6)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (57.4.0)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.7.3)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.12.0)\n","Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (0.39.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (3.1.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (1.1.0)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.1.1)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (3.8.1)\n","Installing collected packages: adversarial-robustness-toolbox\n","Successfully installed adversarial-robustness-toolbox-1.11.0\n"]}]},{"cell_type":"code","metadata":{"id":"1M8cmX6Y_MaU","executionInfo":{"status":"ok","timestamp":1660414225284,"user_tz":-120,"elapsed":8886,"user":{"displayName":"","userId":""}}},"source":["import keras\n","from keras.models import Sequential\n","from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from art.utils import load_mnist"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"3JSlG18GC1RT","executionInfo":{"status":"ok","timestamp":1660414225288,"user_tz":-120,"elapsed":62,"user":{"displayName":"","userId":""}}},"source":["import tensorflow as tf\n","tf.compat.v1.disable_eager_execution()\n","\n","import warnings\n","warnings.filterwarnings('ignore')"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"6k-Q4bnG_SWG","executionInfo":{"status":"ok","timestamp":1660414225292,"user_tz":-120,"elapsed":54,"user":{"displayName":"","userId":""}}},"source":["%matplotlib inline"],"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rYWQUSRC_WLT"},"source":["## Cargar datos"]},{"cell_type":"code","metadata":{"id":"hq1wjOgu_a1U","executionInfo":{"status":"ok","timestamp":1660414226869,"user_tz":-120,"elapsed":1623,"user":{"displayName":"","userId":""}}},"source":["(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gGp89T6o_s3M"},"source":["## Entrenar modelo"]},{"cell_type":"code","metadata":{"id":"gTOks8pDAObr","executionInfo":{"status":"ok","timestamp":1660414226874,"user_tz":-120,"elapsed":56,"user":{"displayName":"","userId":""}}},"source":["from art.estimators.classification import KerasClassifier"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"IwvRBqtoARMM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1685a6b4-10d8-43ee-e67f-0aa885916505","executionInfo":{"status":"ok","timestamp":1660414246003,"user_tz":-120,"elapsed":19164,"user":{"displayName":"","userId":""}}},"source":["model = Sequential()\n","model.add(Conv2D(filters=4, kernel_size=(5, 5), strides=1, activation=\"relu\", input_shape=(28, 28, 1)))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Conv2D(filters=10, kernel_size=(5, 5), strides=1, activation=\"relu\", input_shape=(23, 23, 4)))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Flatten())\n","model.add(Dense(100, activation=\"relu\"))\n","model.add(Dense(10, activation=\"softmax\"))\n","\n","model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=[\"accuracy\"])\n","classifier = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)\n","\n","classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)"],"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Train on 60000 samples\n","Epoch 1/3\n","60000/60000 [==============================] - 10s 174us/sample - loss: 0.1602 - accuracy: 0.9494\n","Epoch 2/3\n","60000/60000 [==============================] - 2s 41us/sample - loss: 0.0849 - accuracy: 0.9744\n","Epoch 3/3\n","60000/60000 [==============================] - 2s 40us/sample - loss: 0.0745 - accuracy: 0.9780\n"]}]},{"cell_type":"code","metadata":{"id":"HoS7HK-8ATSa","colab":{"base_uri":"https://localhost:8080/"},"outputId":"164c759c-d48c-482d-a6d8-b57b3b58312f","executionInfo":{"status":"ok","timestamp":1660414246326,"user_tz":-120,"elapsed":47,"user":{"displayName":"","userId":""}}},"source":["predictions_test = classifier.predict(x_test)\n","accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test examples: {0:.2f}%\".format(accuracy * 100))"],"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test examples: 97.93%\n"]}]},{"cell_type":"markdown","metadata":{"id":"8RjSGz4MAa5n"},"source":["# Ataques de evasión"]},{"cell_type":"markdown","metadata":{"id":"SR-gnZi-Jp0S"},"source":["## Generar ejemplos adversarios"]},{"cell_type":"code","metadata":{"id":"LzFU47WnAd9T","executionInfo":{"status":"ok","timestamp":1660414246330,"user_tz":-120,"elapsed":43,"user":{"displayName":"","userId":""}}},"source":["from art.attacks.evasion import FastGradientMethod, SaliencyMapMethod, CarliniL2Method"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"QH6qevlrAkGh","executionInfo":{"status":"ok","timestamp":1660414248116,"user_tz":-120,"elapsed":1823,"user":{"displayName":"","userId":""}}},"source":["attack_fgm = FastGradientMethod(estimator = classifier, eps = 0.2)\n","x_test_fgm = attack_fgm.generate(x=x_test)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"awFmtipPFFla","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0ecf6dc1-7bb2-4bd9-aabc-e4fb82f3096c","executionInfo":{"status":"ok","timestamp":1660414248120,"user_tz":-120,"elapsed":41,"user":{"displayName":"","userId":""}}},"source":["predictions_test = classifier.predict(x_test_fgm)\n","accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test adversarial examples: {0:.2f}%\".format(accuracy * 100))"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test adversarial examples: 30.93%\n"]}]},{"cell_type":"markdown","metadata":{"id":"THJtvETx17eH"},"source":["## Ataque no dirigido"]},{"cell_type":"code","metadata":{"id":"gYmcNjfG8Ypr","colab":{"base_uri":"https://localhost:8080/"},"outputId":"297f231b-e5d9-4c78-8506-cf31270b132e","executionInfo":{"status":"ok","timestamp":1660414260697,"user_tz":-120,"elapsed":12602,"user":{"displayName":"","userId":""}}},"source":["# Ataque no dirigido\n","best = (100, 0, None, None) # (acc, eps, x_test_fgm, predictions)\n","for eps in np.arange(0.0, 1.0, 0.1):\n"," attack_fgm = FastGradientMethod(estimator = classifier, eps = eps)\n"," x_test_fgm = attack_fgm.generate(x=x_test)\n"," predictions_test = classifier.predict(x_test_fgm)\n"," accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n"," if(accuracy < best[0]):\n"," best = (accuracy, eps, x_test_fgm, predictions_test)\n"," print(\"Accuracy on test adversarial examples: {:.2f}% (eps={:.2f})\".format(accuracy * 100, eps))\n","print(\"Best results: accuracy: {:.2f}% eps={:.2f}\".format(best[0] * 100, best[1]))"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test adversarial examples: 97.93% (eps=0.00)\n","Accuracy on test adversarial examples: 60.92% (eps=0.10)\n","Accuracy on test adversarial examples: 30.93% (eps=0.20)\n","Accuracy on test adversarial examples: 19.45% (eps=0.30)\n","Accuracy on test adversarial examples: 14.61% (eps=0.40)\n","Accuracy on test adversarial examples: 11.29% (eps=0.50)\n","Accuracy on test adversarial examples: 9.55% (eps=0.60)\n","Accuracy on test adversarial examples: 9.30% (eps=0.70)\n","Accuracy on test adversarial examples: 9.13% (eps=0.80)\n","Accuracy on test adversarial examples: 8.99% (eps=0.90)\n","Best results: accuracy: 8.99% eps=0.90\n"]}]},{"cell_type":"markdown","metadata":{"id":"noyhMckN1_JK"},"source":["## Ataque dirigido"]},{"cell_type":"code","metadata":{"id":"O3Lnxy3W7fqe","executionInfo":{"status":"ok","timestamp":1660414260732,"user_tz":-120,"elapsed":89,"user":{"displayName":"","userId":""}}},"source":["from art.utils import to_categorical"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZWISeIXx-0sy","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a8b7c81e-da6d-482b-e046-22039336b539","executionInfo":{"status":"ok","timestamp":1660414268802,"user_tz":-120,"elapsed":8150,"user":{"displayName":"","userId":""}}},"source":["# Ataque dirigido\n","y = np.ones(len(y_test))*9\n","y_targeted = to_categorical(y, nb_classes=10)\n","best_targeted = (0, 0.1, None, None) # (acc, eps, x_test_fgm, predictions)\n","for eps in np.arange(0.1, 1.0, 0.1):\n"," attack_fgm = FastGradientMethod(estimator = classifier, eps = eps, targeted=True)\n"," x_test_fgm = attack_fgm.generate(x=x_test, y=y_targeted)\n"," predictions_test = classifier.predict(x_test_fgm)\n"," accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_targeted, axis=1)) / len(y_targeted)\n"," if(accuracy > best_targeted[0]):\n"," best_targeted = (accuracy, eps, x_test_fgm, predictions_test)\n"," print(\"Accuracy on test adversarial examples: {:.2f}% (eps={:.2f})\".format(accuracy * 100, eps))\n","print(\"Best results (targeted): accuracy: {:.2f}% eps={:.2f}\".format(best_targeted[0] * 100, best_targeted[1]))"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test adversarial examples: 24.30% (eps=0.10)\n","Accuracy on test adversarial examples: 35.35% (eps=0.20)\n","Accuracy on test adversarial examples: 36.33% (eps=0.30)\n","Accuracy on test adversarial examples: 32.73% (eps=0.40)\n","Accuracy on test adversarial examples: 27.97% (eps=0.50)\n","Accuracy on test adversarial examples: 22.94% (eps=0.60)\n","Accuracy on test adversarial examples: 18.24% (eps=0.70)\n","Accuracy on test adversarial examples: 14.55% (eps=0.80)\n","Accuracy on test adversarial examples: 11.41% (eps=0.90)\n","Best results (targeted): accuracy: 36.33% eps=0.30\n"]}]},{"cell_type":"code","metadata":{"id":"udwfxbJoE9hM","colab":{"base_uri":"https://localhost:8080/","height":290},"outputId":"ca7e1a72-8d23-432b-baec-3b58d97fab41","executionInfo":{"status":"ok","timestamp":1660414268806,"user_tz":-120,"elapsed":146,"user":{"displayName":"","userId":""}}},"source":["sample = 1234\n","fig = plt.figure(figsize=(20,10))\n","ax = fig.add_subplot(1, 4, 1)\n","ax.imshow(x_test[sample].reshape((28, 28)), cmap='gray', interpolation='none')\n","ax.set_title(\"Original: {}\".format(np.argmax(y_test[sample])))\n","ax.axis('off')\n","ax = fig.add_subplot(1, 4, 2)\n","ax.imshow(best[2][sample].reshape((28, 28)), cmap='gray', interpolation='none')\n","ax.set_title(\"Adversarial (FGSM): {}\".format(np.argmax(best[3][sample])))\n","ax.axis('off')\n","ax = fig.add_subplot(1, 4, 3)\n","ax.imshow(best_targeted[2][sample].reshape((28, 28)), cmap='gray', interpolation='none')\n","ax.set_title(\"Adversarial (FGSM - targeted): {}\".format(np.argmax(best_targeted[3][sample])))\n","ax.axis('off')\n","fig.show()"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"O_GYhKOCB6SS"},"source":["## Otros métodos de ataque"]},{"cell_type":"code","metadata":{"id":"9w5pLUQrAo4e","executionInfo":{"status":"ok","timestamp":1660414268811,"user_tz":-120,"elapsed":126,"user":{"displayName":"","userId":""}}},"source":["# Más métodos se pueden encontrar en \n","# https://adversarial-robustness-toolbox.readthedocs.io/en/latest/modules/attacks/evasion.html\n","\n","# Estos métodos tardan bastante más tiempo en ejecutar.\n","\n","# JSMA\n","# attack_jsma = SaliencyMapMethod(classifier = classifier, theta = 0.1)\n","# x_test_jsma = attack_jsma.generate(x=x_test)\n","\n","# Carlini&Wagner\n","# attack_cw2 = CarliniL2Method(classifier = classifier)\n","# x_test_cw2 = attack_cw2.generate(x=x_test)"],"execution_count":16,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bXZyqv27Ag9N"},"source":["## Entrenamiento adversario"]},{"cell_type":"code","metadata":{"id":"lmH04l3D3wlr","executionInfo":{"status":"ok","timestamp":1660414270038,"user_tz":-120,"elapsed":1349,"user":{"displayName":"","userId":""}}},"source":["(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"id":"hZtOAlI5BGX2","executionInfo":{"status":"ok","timestamp":1660414278717,"user_tz":-120,"elapsed":8692,"user":{"displayName":"","userId":""}}},"source":["attack_fgm = FastGradientMethod(estimator = classifier, eps = 0.6)\n","x_train_fgm = attack_fgm.generate(x=x_train)\n","x_test_fgm = attack_fgm.generate(x=x_test)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"id":"BggbW6KoBJNk","executionInfo":{"status":"ok","timestamp":1660414278720,"user_tz":-120,"elapsed":57,"user":{"displayName":"","userId":""}}},"source":["x_train = np.append(x_train, x_train_fgm, axis=0)\n","y_train = np.append(y_train, y_train, axis=0)"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"id":"IOdjNpvJBLIz","colab":{"base_uri":"https://localhost:8080/"},"outputId":"223c9c89-43ad-4e61-92d1-64bcef380ac1","executionInfo":{"status":"ok","timestamp":1660414294659,"user_tz":-120,"elapsed":15988,"user":{"displayName":"","userId":""}}},"source":["model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=[\"accuracy\"])\n","\n","classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Train on 120000 samples\n","Epoch 1/3\n","120000/120000 [==============================] - 5s 42us/sample - loss: 0.3439 - accuracy: 0.8920\n","Epoch 2/3\n","120000/120000 [==============================] - 5s 40us/sample - loss: 0.2003 - accuracy: 0.9398\n","Epoch 3/3\n","120000/120000 [==============================] - 5s 40us/sample - loss: 0.1799 - accuracy: 0.9475\n"]}]},{"cell_type":"code","metadata":{"id":"2WLDCQoIBRiT","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e438fefe-e063-43d7-857f-ce62f5eda1e0","executionInfo":{"status":"ok","timestamp":1660414295115,"user_tz":-120,"elapsed":572,"user":{"displayName":"","userId":""}}},"source":["predictions_test = classifier.predict(x_test)\n","accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test examples: {0:.2f}%\".format(accuracy * 100))\n","\n","predictions_fsm = classifier.predict(x_test_fgm)\n","accuracy = np.sum(np.argmax(predictions_fsm, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on adversarial test examples for FSGM attack: {0:.2f}%\".format(accuracy * 100))"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test examples: 96.66%\n","Accuracy on adversarial test examples for FSGM attack: 92.59%\n"]}]},{"cell_type":"markdown","metadata":{"id":"FaoK60dJcHcy"},"source":["## Entrenamiento adversario de forma nativa"]},{"cell_type":"code","metadata":{"id":"brtrAWaCUSmz","executionInfo":{"status":"ok","timestamp":1660414295119,"user_tz":-120,"elapsed":56,"user":{"displayName":"","userId":""}}},"source":["from art.defences.trainer import AdversarialTrainer"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"AE_5ZSKLUuCu","executionInfo":{"status":"ok","timestamp":1660414295123,"user_tz":-120,"elapsed":55,"user":{"displayName":"","userId":""}}},"source":["model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=[\"accuracy\"])"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"7zL6s6qfUZ4Y","executionInfo":{"status":"ok","timestamp":1660414295127,"user_tz":-120,"elapsed":56,"user":{"displayName":"","userId":""}}},"source":["defence = AdversarialTrainer(classifier=classifier, attacks=attack_fgm, ratio=0.6)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"id":"TXRJQTipVFuu","executionInfo":{"status":"ok","timestamp":1660414296008,"user_tz":-120,"elapsed":933,"user":{"displayName":"","userId":""}}},"source":["(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"id":"ww6WB1BjU59K","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["957b3026b07c4e7e9a42e8ef14260382","96c6a65bcd1c4836b6f7c1c84c570625","8b496af3151b4d7393e4490eed7fdef7","40f6d51c181046ce98ef8738d9f78d5d","6ccb0f1ffbc948f5a9592d818dba7162","7f9b5c3b79e24b828cc3ded70aba5020","7630fdf012434da38daf5838f44fe906","7d79ab81ff694ac6af0d0d813bf78e8b","1ed56bf8c02b496e85fa4d13841803e0","d599721ee9a54a92881c481071384d2f","d24c9b1458fb4b7e86f77ca374be1390","3365623644cb41989eb58e1217284b3d","ab23a9856e344a62901e190cee0ac1e1","b8290ce28f3c42d381e5e33856322792","ae98842b724d43ee970629f55ce2679a","e1b81b22494344a287f1fce48d9f80bd","c4461a22652741bda828ec56e9d050ea","c955da81bb5048f7817f4eb8f0a3ccee","eb8ed18be9de4099af881b8e00f65e2d","f9d21da6c8e74e51a82a5a4d451b5daf","226309a557bf4f6a9d151880987ffbe0","4bbff80976144ae99decce5c6aae554e"]},"outputId":"0f5ec792-39c2-46b0-fbbf-1165f9c1ddd5","executionInfo":{"status":"ok","timestamp":1660414313384,"user_tz":-120,"elapsed":17620,"user":{"displayName":"","userId":""}}},"source":["defence.fit(x=x_train, y=y_train, nb_epochs=3)"],"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":["Precompute adv samples: 0%| | 0/1 [00:00=0.53.1\n", - " Downloading numba-0.54.1-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (3.3 MB)\n", - "\u001b[K |████████████████████████████████| 3.3 MB 30.3 MB/s \n", - "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (57.4.0)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (4.62.3)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.15.0)\n", - "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.19.5)\n", - "Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.0.1)\n", - "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.4.1)\n", - "Collecting llvmlite<0.38,>=0.37.0rc1\n", - " Downloading llvmlite-0.37.0-cp37-cp37m-manylinux2014_x86_64.whl (26.3 MB)\n", - "\u001b[K |████████████████████████████████| 26.3 MB 1.9 MB/s \n", - "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.8.1) (1.1.0)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.8.1) (3.0.0)\n", - "Installing collected packages: llvmlite, numba, adversarial-robustness-toolbox\n", - " Attempting uninstall: llvmlite\n", - " Found existing installation: llvmlite 0.34.0\n", - " Uninstalling llvmlite-0.34.0:\n", - " Successfully uninstalled llvmlite-0.34.0\n", - " Attempting uninstall: numba\n", - " Found existing installation: numba 0.51.2\n", - " Uninstalling numba-0.51.2:\n", - " Successfully uninstalled numba-0.51.2\n", - "Successfully installed adversarial-robustness-toolbox-1.8.1 llvmlite-0.37.0 numba-0.54.1\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1M8cmX6Y_MaU" - }, - "source": [ - "import keras\n", - "import tensorflow as tf\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from art.utils import load_mnist" - ], - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "3JSlG18GC1RT" - }, - "source": [ - "from tensorflow.python.framework.ops import disable_eager_execution\n", - "disable_eager_execution()\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ], - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "6k-Q4bnG_SWG" - }, - "source": [ - "%matplotlib inline" - ], - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYWQUSRC_WLT" - }, - "source": [ - "## Cargar datos" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hq1wjOgu_a1U" - }, - "source": [ - "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()" - ], - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gGp89T6o_s3M" - }, - "source": [ - "## Entrenar modelo" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "gTOks8pDAObr" - }, - "source": [ - "from art.estimators.classification import KerasClassifier" - ], - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "IwvRBqtoARMM", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "37549fca-7a37-4426-c2ff-edd73bb4ce19" - }, - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dense(10, activation='softmax'))\n", - "\n", - "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", - "\n", - "victim = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)\n", - "\n", - "victim.fit(x_train, y_train, batch_size=128, nb_epochs=5)" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 60000 samples\n", - "Epoch 1/5\n", - "60000/60000 [==============================] - 17s 284us/sample - loss: 0.2147 - accuracy: 0.9353\n", - "Epoch 2/5\n", - "60000/60000 [==============================] - 4s 71us/sample - loss: 0.0648 - accuracy: 0.9791\n", - "Epoch 3/5\n", - "60000/60000 [==============================] - 4s 71us/sample - loss: 0.0452 - accuracy: 0.9860\n", - "Epoch 4/5\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0364 - accuracy: 0.9887\n", - "Epoch 5/5\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0282 - accuracy: 0.9911\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "HoS7HK-8ATSa", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7b576155-d0dd-4cdb-816d-e3c0d1b3df97" - }, - "source": [ - "predictions_test = victim.predict(x_test)\n", - "accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test examples: {:.2f}%\".format(accuracy * 100))" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test examples: 99.14%\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2I0uYfvL8h2p" - }, - "source": [ - "## Ataque de extracción" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "6kQKxhSyAeCE" - }, - "source": [ - "# Más ataques en\n", - "# https://adversarial-robustness-toolbox.readthedocs.io/en/latest/modules/attacks/extraction.html\n", - "\n", - "from art.attacks.extraction import CopycatCNN" - ], - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "bmJhHIOx8lvD" - }, - "source": [ - "max_requests = 5000 \n", - "shuffle = np.random.permutation(len(x_test))\n", - "x_stolen = x_test[shuffle[:max_requests]]\n", - "y_stolen = y_test[shuffle[:max_requests]]" - ], - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "JH5tb4UZ9lfB" - }, - "source": [ - "model_stolen = Sequential()\n", - "model_stolen.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "model_stolen.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model_stolen.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model_stolen.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model_stolen.add(Dropout(0.25))\n", - "model_stolen.add(Flatten())\n", - "model_stolen.add(Dense(128, activation='relu'))\n", - "model_stolen.add(Dense(10, activation='softmax'))\n", - "\n", - "model_stolen.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])" - ], - "execution_count": 11, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "3WHYWXWJ9QVd", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "e88bf9df-ca35-4607-992f-13f230632c01" - }, - "source": [ - "attack = CopycatCNN(classifier=victim, nb_epochs=5, nb_stolen=max_requests, use_probability=True)\n", - "classifier_stolen = KerasClassifier(model_stolen, clip_values=(0, 1), use_logits=False)\n", - "classifier_stolen = attack.extract(x_stolen, y_stolen, thieved_classifier=classifier_stolen)" - ], - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 5000 samples\n", - "Epoch 1/5\n", - "5000/5000 [==============================] - 25s 5ms/sample - loss: 0.2969 - accuracy: 0.9088\n", - "Epoch 2/5\n", - "5000/5000 [==============================] - 25s 5ms/sample - loss: 0.1248 - accuracy: 0.9654\n", - "Epoch 3/5\n", - "5000/5000 [==============================] - 25s 5ms/sample - loss: 0.0975 - accuracy: 0.9762\n", - "Epoch 4/5\n", - "5000/5000 [==============================] - 25s 5ms/sample - loss: 0.0835 - accuracy: 0.9810\n", - "Epoch 5/5\n", - "5000/5000 [==============================] - 25s 5ms/sample - loss: 0.0792 - accuracy: 0.9806\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "V8Yx2ttX91DS", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5d4bfcfd-77f0-4a28-fae4-91ac579f4f8b" - }, - "source": [ - "predictions_stolen = classifier_stolen.predict(x_test)\n", - "accuracy = np.sum(np.argmax(predictions_stolen, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test examples (stolen model): {:.2f}%\".format(accuracy * 100))" - ], - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test examples (stolen model): 98.58%\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4mFwoBCTIW3e" - }, - "source": [ - "# Defensas" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "zGcq5sH2zJ1_" - }, - "source": [ - "# Más defensas en\n", - "# https://adversarial-robustness-toolbox.readthedocs.io/en/latest/modules/defences/postprocessor.html\n", - "\n", - "from art.defences.postprocessor import Rounded, GaussianNoise, ReverseSigmoid" - ], - "execution_count": 14, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "G9s4tJLvzLTi" - }, - "source": [ - "postprocessor_rounded = Rounded(decimals=1)" - ], - "execution_count": 16, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "_dHYz36Q1hsB" - }, - "source": [ - "postprocessor_gaussian = GaussianNoise(scale=0.1)" - ], - "execution_count": 17, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "-68Yu_oRDFN1" - }, - "source": [ - "postprocessor_reverse_sigmoid = ReverseSigmoid(beta=1.0, gamma=0.5)" - ], - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "mcpHJCaP22Qg", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "86efb043-5939-4eb2-aa4c-2eee9fd12075" - }, - "source": [ - "victim_defense = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False, postprocessing_defences=postprocessor_reverse_sigmoid)\n", - "victim_defense.fit(x_train, y_train, batch_size=128, nb_epochs=5)" - ], - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 60000 samples\n", - "Epoch 1/5\n", - "60000/60000 [==============================] - 4s 73us/sample - loss: 0.0257 - accuracy: 0.9918\n", - "Epoch 2/5\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0218 - accuracy: 0.9928\n", - "Epoch 3/5\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0174 - accuracy: 0.9942\n", - "Epoch 4/5\n", - "60000/60000 [==============================] - 4s 71us/sample - loss: 0.0151 - accuracy: 0.9948\n", - "Epoch 5/5\n", - "60000/60000 [==============================] - 4s 71us/sample - loss: 0.0141 - accuracy: 0.9954\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "3qmENF0QHb2D", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d2dc7a0a-3faf-4be8-d85b-3f78f5d99cc1" - }, - "source": [ - "predictions_victim_defense = victim_defense.predict(x_test)\n", - "accuracy = np.sum(np.argmax(predictions_victim_defense, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test examples (protected): {:.2f}%\".format(accuracy * 100))" - ], - "execution_count": 20, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test examples (protected): 99.19%\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "OTJfO4OAEWtG" - }, - "source": [ - "model_stolen_protected = Sequential()\n", - "model_stolen_protected.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "model_stolen_protected.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model_stolen_protected.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model_stolen_protected.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model_stolen_protected.add(Dropout(0.25))\n", - "model_stolen_protected.add(Flatten())\n", - "model_stolen_protected.add(Dense(128, activation='relu'))\n", - "model_stolen_protected.add(Dense(10, activation='softmax'))\n", - "model_stolen_protected.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])" - ], - "execution_count": 21, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "VmwEMUD2BD6W", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "0bb8421e-78ea-48a8-9875-dfd6cf92e124" - }, - "source": [ - "attack_protected = CopycatCNN(classifier=victim_defense, nb_epochs=5, nb_stolen=max_requests, use_probability=True)\n", - "classifier_stolen_protected = KerasClassifier(model_stolen_protected, clip_values=(0, 1), use_logits=False)\n", - "classifier_stolen_protected = attack_protected.extract(x_stolen, y_stolen, thieved_classifier=classifier_stolen_protected)" - ], - "execution_count": 22, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 5000 samples\n", - "Epoch 1/5\n", - "5000/5000 [==============================] - 25s 5ms/sample - loss: 2.3026 - accuracy: 0.1034\n", - "Epoch 2/5\n", - "5000/5000 [==============================] - 24s 5ms/sample - loss: 2.3026 - accuracy: 0.1018\n", - "Epoch 3/5\n", - "5000/5000 [==============================] - 24s 5ms/sample - loss: 2.3026 - accuracy: 0.1052\n", - "Epoch 4/5\n", - "5000/5000 [==============================] - 24s 5ms/sample - loss: 2.3026 - accuracy: 0.1002\n", - "Epoch 5/5\n", - "5000/5000 [==============================] - 24s 5ms/sample - loss: 2.3026 - accuracy: 0.0920\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "k8D6kU5UIKij", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "195a3c91-7c22-4475-c331-f5e97c71c39f" - }, - "source": [ - "predictions_stolen_protected = classifier_stolen_protected.predict(x_test)\n", - "accuracy = np.sum(np.argmax(predictions_stolen_protected, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test examples against protected model (stolen model): {:.2f}%\".format(accuracy * 100))" - ], - "execution_count": 23, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test examples against protected model (stolen model): 9.74%\n" - ] - } - ] - } - ] -} \ No newline at end of file +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Copia de Copia de extraction","provenance":[{"file_id":"1EBB1Skj76TzUUakJHjpsd4d2MrWFbDJ6","timestamp":1660403618547},{"file_id":"https://github.com/jiep/adversarial-machine-learning/blob/main/notebooks/extraction.ipynb","timestamp":1660402640921}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"CLrkyRc6-rD5"},"source":["# Adversarial Robustness Toolkit (ART)\n","\n","* Documentación: https://adversarial-robustness-toolbox.readthedocs.io/en/latest/\n","* Código: https://github.com/Trusted-AI/adversarial-robustness-toolbox\n","* Ejemplos: https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/examples"]},{"cell_type":"markdown","metadata":{"id":"NuoZ5bhE-2_0"},"source":["## Instalación"]},{"cell_type":"code","metadata":{"id":"Zua3aciV-O5K","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f157d2f4-1249-46c9-da02-8fb5100c32b8","executionInfo":{"status":"ok","timestamp":1660403367321,"user_tz":-120,"elapsed":2818,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["!pip install adversarial-robustness-toolbox==1.11.0"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: adversarial-robustness-toolbox==1.11.0 in /usr/local/lib/python3.7/dist-packages (1.11.0)\n","Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.21.6)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.7.3)\n","Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.0.2)\n","Requirement already satisfied: numba>=0.53.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (0.56.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (4.64.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.15.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (57.4.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.12.0)\n","Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (0.39.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (3.1.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (1.1.0)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.1.1)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (3.8.1)\n"]}]},{"cell_type":"code","metadata":{"id":"1M8cmX6Y_MaU","executionInfo":{"status":"ok","timestamp":1660403372536,"user_tz":-120,"elapsed":5251,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["import keras\n","import tensorflow as tf\n","from keras.models import Sequential\n","from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from art.utils import load_mnist"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"3JSlG18GC1RT","executionInfo":{"status":"ok","timestamp":1660403372539,"user_tz":-120,"elapsed":121,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["from tensorflow.python.framework.ops import disable_eager_execution\n","disable_eager_execution()\n","\n","import warnings\n","warnings.filterwarnings('ignore')"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"6k-Q4bnG_SWG","executionInfo":{"status":"ok","timestamp":1660403372542,"user_tz":-120,"elapsed":115,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["%matplotlib inline"],"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rYWQUSRC_WLT"},"source":["## Cargar datos"]},{"cell_type":"code","metadata":{"id":"hq1wjOgu_a1U","executionInfo":{"status":"ok","timestamp":1660403373205,"user_tz":-120,"elapsed":774,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gGp89T6o_s3M"},"source":["## Entrenar modelo"]},{"cell_type":"code","metadata":{"id":"gTOks8pDAObr","executionInfo":{"status":"ok","timestamp":1660403373210,"user_tz":-120,"elapsed":47,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["from art.estimators.classification import KerasClassifier"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"IwvRBqtoARMM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"897cb9bd-a70e-43a1-eefb-768320350e6d","executionInfo":{"status":"ok","timestamp":1660403391473,"user_tz":-120,"elapsed":18307,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["model = Sequential()\n","model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Conv2D(64, (3, 3), activation='relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Dropout(0.25))\n","model.add(Flatten())\n","model.add(Dense(128, activation='relu'))\n","model.add(Dense(10, activation='softmax'))\n","\n","model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n","\n","victim = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)\n","\n","victim.fit(x_train, y_train, batch_size=128, nb_epochs=5)"],"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Train on 60000 samples\n","Epoch 1/5\n","60000/60000 [==============================] - 4s 73us/sample - loss: 0.2241 - accuracy: 0.9337\n","Epoch 2/5\n","60000/60000 [==============================] - 2s 39us/sample - loss: 0.0634 - accuracy: 0.9806\n","Epoch 3/5\n","60000/60000 [==============================] - 3s 55us/sample - loss: 0.0453 - accuracy: 0.9863\n","Epoch 4/5\n","60000/60000 [==============================] - 4s 65us/sample - loss: 0.0359 - accuracy: 0.9885\n","Epoch 5/5\n","60000/60000 [==============================] - 4s 62us/sample - loss: 0.0290 - accuracy: 0.9908\n"]}]},{"cell_type":"code","metadata":{"id":"HoS7HK-8ATSa","colab":{"base_uri":"https://localhost:8080/"},"outputId":"4c6c35fb-1dfd-4368-ed1c-ef59d2d8efc2","executionInfo":{"status":"ok","timestamp":1660403391479,"user_tz":-120,"elapsed":156,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["predictions_test = victim.predict(x_test)\n","accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test examples: {:.2f}%\".format(accuracy * 100))"],"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test examples: 99.02%\n"]}]},{"cell_type":"markdown","metadata":{"id":"2I0uYfvL8h2p"},"source":["## Ataque de extracción"]},{"cell_type":"code","metadata":{"id":"6kQKxhSyAeCE","executionInfo":{"status":"ok","timestamp":1660403391484,"user_tz":-120,"elapsed":110,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["# Más ataques en\n","# https://adversarial-robustness-toolbox.readthedocs.io/en/latest/modules/attacks/extraction.html\n","\n","from art.attacks.extraction import CopycatCNN"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"bmJhHIOx8lvD","executionInfo":{"status":"ok","timestamp":1660403391490,"user_tz":-120,"elapsed":113,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["max_requests = 5000 \n","shuffle = np.random.permutation(len(x_test))\n","x_stolen = x_test[shuffle[:max_requests]]\n","y_stolen = y_test[shuffle[:max_requests]]"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"JH5tb4UZ9lfB","executionInfo":{"status":"ok","timestamp":1660403392023,"user_tz":-120,"elapsed":643,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["model_stolen = Sequential()\n","model_stolen.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n","model_stolen.add(MaxPooling2D(pool_size=(2, 2)))\n","model_stolen.add(Conv2D(64, (3, 3), activation='relu'))\n","model_stolen.add(MaxPooling2D(pool_size=(2, 2)))\n","model_stolen.add(Dropout(0.25))\n","model_stolen.add(Flatten())\n","model_stolen.add(Dense(128, activation='relu'))\n","model_stolen.add(Dense(10, activation='softmax'))\n","\n","model_stolen.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"3WHYWXWJ9QVd","colab":{"base_uri":"https://localhost:8080/"},"outputId":"131eb23c-c34b-4722-c495-e4d6beb86e92","executionInfo":{"status":"ok","timestamp":1660403478555,"user_tz":-120,"elapsed":86567,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["attack = CopycatCNN(classifier=victim, nb_epochs=5, nb_stolen=max_requests, use_probability=True)\n","classifier_stolen = KerasClassifier(model_stolen, clip_values=(0, 1), use_logits=False)\n","classifier_stolen = attack.extract(x_stolen, y_stolen, thieved_classifier=classifier_stolen)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:art.attacks.extraction.copycat_cnn:This attack does not use the provided label y.\n"]},{"output_type":"stream","name":"stdout","text":["Train on 5000 samples\n","Epoch 1/5\n","5000/5000 [==============================] - 16s 3ms/sample - loss: 0.3061 - accuracy: 0.9062\n","Epoch 2/5\n","5000/5000 [==============================] - 16s 3ms/sample - loss: 0.1193 - accuracy: 0.9680\n","Epoch 3/5\n","5000/5000 [==============================] - 17s 3ms/sample - loss: 0.0907 - accuracy: 0.9782\n","Epoch 4/5\n","5000/5000 [==============================] - 16s 3ms/sample - loss: 0.0762 - accuracy: 0.9836\n","Epoch 5/5\n","5000/5000 [==============================] - 17s 3ms/sample - loss: 0.0777 - accuracy: 0.9808\n"]}]},{"cell_type":"code","metadata":{"id":"V8Yx2ttX91DS","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1bd2c6ae-efc0-4493-dea5-e7f12f0ac16e","executionInfo":{"status":"ok","timestamp":1660403479196,"user_tz":-120,"elapsed":759,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["predictions_stolen = classifier_stolen.predict(x_test)\n","accuracy = np.sum(np.argmax(predictions_stolen, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test examples (stolen model): {:.2f}%\".format(accuracy * 100))"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test examples (stolen model): 98.38%\n"]}]},{"cell_type":"markdown","metadata":{"id":"4mFwoBCTIW3e"},"source":["# Defensas"]},{"cell_type":"code","metadata":{"id":"zGcq5sH2zJ1_","executionInfo":{"status":"ok","timestamp":1660403479200,"user_tz":-120,"elapsed":89,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["# Más defensas en\n","# https://adversarial-robustness-toolbox.readthedocs.io/en/latest/modules/defences/postprocessor.html\n","\n","from art.defences.postprocessor import Rounded, GaussianNoise, ReverseSigmoid"],"execution_count":14,"outputs":[]},{"cell_type":"code","metadata":{"id":"G9s4tJLvzLTi","executionInfo":{"status":"ok","timestamp":1660403479216,"user_tz":-120,"elapsed":101,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["postprocessor_rounded = Rounded(decimals=1)"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"id":"_dHYz36Q1hsB","executionInfo":{"status":"ok","timestamp":1660403479219,"user_tz":-120,"elapsed":101,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["postprocessor_gaussian = GaussianNoise(scale=0.1)"],"execution_count":16,"outputs":[]},{"cell_type":"code","metadata":{"id":"-68Yu_oRDFN1","executionInfo":{"status":"ok","timestamp":1660403479223,"user_tz":-120,"elapsed":102,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["postprocessor_reverse_sigmoid = ReverseSigmoid(beta=1.0, gamma=0.5)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"id":"mcpHJCaP22Qg","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e4775810-ba2f-4711-ba8b-d5f16c8cbbdd","executionInfo":{"status":"ok","timestamp":1660403490852,"user_tz":-120,"elapsed":11722,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["victim_defense = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False, postprocessing_defences=postprocessor_reverse_sigmoid)\n","victim_defense.fit(x_train, y_train, batch_size=128, nb_epochs=5)"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Train on 60000 samples\n","Epoch 1/5\n","60000/60000 [==============================] - 2s 39us/sample - loss: 0.0244 - accuracy: 0.9923\n","Epoch 2/5\n","60000/60000 [==============================] - 2s 38us/sample - loss: 0.0217 - accuracy: 0.9933\n","Epoch 3/5\n","60000/60000 [==============================] - 2s 38us/sample - loss: 0.0188 - accuracy: 0.9941\n","Epoch 4/5\n","60000/60000 [==============================] - 2s 39us/sample - loss: 0.0158 - accuracy: 0.9948\n","Epoch 5/5\n","60000/60000 [==============================] - 2s 38us/sample - loss: 0.0136 - accuracy: 0.9955\n"]}]},{"cell_type":"code","metadata":{"id":"3qmENF0QHb2D","colab":{"base_uri":"https://localhost:8080/"},"outputId":"311689f1-3c45-41d0-9798-493fb1fb3859","executionInfo":{"status":"ok","timestamp":1660403491331,"user_tz":-120,"elapsed":579,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["predictions_victim_defense = victim_defense.predict(x_test)\n","accuracy = np.sum(np.argmax(predictions_victim_defense, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test examples (protected): {:.2f}%\".format(accuracy * 100))"],"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test examples (protected): 99.09%\n"]}]},{"cell_type":"code","metadata":{"id":"OTJfO4OAEWtG","executionInfo":{"status":"ok","timestamp":1660403491335,"user_tz":-120,"elapsed":43,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["model_stolen_protected = Sequential()\n","model_stolen_protected.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n","model_stolen_protected.add(MaxPooling2D(pool_size=(2, 2)))\n","model_stolen_protected.add(Conv2D(64, (3, 3), activation='relu'))\n","model_stolen_protected.add(MaxPooling2D(pool_size=(2, 2)))\n","model_stolen_protected.add(Dropout(0.25))\n","model_stolen_protected.add(Flatten())\n","model_stolen_protected.add(Dense(128, activation='relu'))\n","model_stolen_protected.add(Dense(10, activation='softmax'))\n","model_stolen_protected.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"VmwEMUD2BD6W","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f0e07410-3f36-41bd-a41b-462596e4b47b","executionInfo":{"status":"ok","timestamp":1660403578326,"user_tz":-120,"elapsed":87028,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["attack_protected = CopycatCNN(classifier=victim_defense, nb_epochs=5, nb_stolen=max_requests, use_probability=True)\n","classifier_stolen_protected = KerasClassifier(model_stolen_protected, clip_values=(0, 1), use_logits=False)\n","classifier_stolen_protected = attack_protected.extract(x_stolen, y_stolen, thieved_classifier=classifier_stolen_protected)"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:art.attacks.extraction.copycat_cnn:This attack does not use the provided label y.\n"]},{"output_type":"stream","name":"stdout","text":["Train on 5000 samples\n","Epoch 1/5\n","5000/5000 [==============================] - 16s 3ms/sample - loss: 2.3026 - accuracy: 0.1040\n","Epoch 2/5\n","5000/5000 [==============================] - 16s 3ms/sample - loss: 2.3026 - accuracy: 0.1022\n","Epoch 3/5\n","5000/5000 [==============================] - 16s 3ms/sample - loss: 2.3026 - accuracy: 0.0972\n","Epoch 4/5\n","5000/5000 [==============================] - 17s 3ms/sample - loss: 2.3026 - accuracy: 0.0890\n","Epoch 5/5\n","5000/5000 [==============================] - 16s 3ms/sample - loss: 2.3026 - accuracy: 0.0952\n"]}]},{"cell_type":"code","metadata":{"id":"k8D6kU5UIKij","colab":{"base_uri":"https://localhost:8080/"},"outputId":"17b32f57-7b22-4c7f-c6b4-381ae723e526","executionInfo":{"status":"ok","timestamp":1660403578911,"user_tz":-120,"elapsed":695,"user":{"displayName":"José Ignacio Escribano Pablos","userId":"14595616013044218709"}}},"source":["predictions_stolen_protected = classifier_stolen_protected.predict(x_test)\n","accuracy = np.sum(np.argmax(predictions_stolen_protected, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test examples against protected model (stolen model): {:.2f}%\".format(accuracy * 100))"],"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test examples against protected model (stolen model): 9.80%\n"]}]}]} \ No newline at end of file diff --git a/notebooks/inversion.ipynb b/notebooks/inversion.ipynb index 5fd1993..71b36c2 100644 --- a/notebooks/inversion.ipynb +++ b/notebooks/inversion.ipynb @@ -1,2262 +1 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "inversion", - "provenance": [], - "collapsed_sections": [], - "toc_visible": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "accelerator": "GPU", - 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"grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "7ec315ed80e244b09d1cd7a4f111fe0c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "3ff84f41bdc7499bba2d7e079a812358": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - } - } - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "CLrkyRc6-rD5" - }, - "source": [ - "# Adversarial Robustness Toolkit\n", - "\n", - "* Documentación: https://adversarial-robustness-toolbox.readthedocs.io/en/latest/\n", - "* Código: https://github.com/Trusted-AI/adversarial-robustness-toolbox\n", - "* Ejemplos: https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/examples" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NuoZ5bhE-2_0" - }, - "source": [ - "## Instalación" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Zua3aciV-O5K", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "10ff3d19-9baa-4182-8975-f94c24ba5602" - }, - "source": [ - "!pip install adversarial-robustness-toolbox==1.8.1" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting adversarial-robustness-toolbox==1.8.1\n", - " Downloading adversarial_robustness_toolbox-1.8.1-py3-none-any.whl (1.1 MB)\n", - "\u001b[?25l\r\u001b[K |▎ | 10 kB 25.0 MB/s eta 0:00:01\r\u001b[K |▋ | 20 kB 29.5 MB/s eta 0:00:01\r\u001b[K |█ | 30 kB 26.7 MB/s eta 0:00:01\r\u001b[K |█▏ | 40 kB 19.7 MB/s eta 0:00:01\r\u001b[K |█▌ | 51 kB 15.3 MB/s eta 0:00:01\r\u001b[K |█▉ | 61 kB 11.2 MB/s eta 0:00:01\r\u001b[K |██ | 71 kB 12.2 MB/s eta 0:00:01\r\u001b[K |██▍ | 81 kB 9.6 MB/s eta 0:00:01\r\u001b[K |██▊ | 92 kB 10.5 MB/s eta 0:00:01\r\u001b[K |███ | 102 kB 11.3 MB/s eta 0:00:01\r\u001b[K |███▎ | 112 kB 11.3 MB/s eta 0:00:01\r\u001b[K |███▋ | 122 kB 11.3 MB/s eta 0:00:01\r\u001b[K |███▉ | 133 kB 11.3 MB/s eta 0:00:01\r\u001b[K |████▏ | 143 kB 11.3 MB/s eta 0:00:01\r\u001b[K |████▌ | 153 kB 11.3 MB/s eta 0:00:01\r\u001b[K |████▊ | 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MaxPooling2D, Dropout\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from art.utils import load_mnist" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "3JSlG18GC1RT" - }, - "source": [ - "import tensorflow as tf\n", - "tf.compat.v1.disable_eager_execution()\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "6k-Q4bnG_SWG" - }, - "source": [ - "%matplotlib inline" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYWQUSRC_WLT" - }, - "source": [ - "## Cargar datos" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hq1wjOgu_a1U" - }, - "source": [ - "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gGp89T6o_s3M" - }, - "source": [ - "## Entrenar modelo" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "gTOks8pDAObr" - }, - "source": [ - "from art.estimators.classification import KerasClassifier" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "IwvRBqtoARMM", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "73aaa270-ef1e-41a3-a733-3006e17de280" - }, - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dense(10, activation='softmax'))\n", - "\n", - "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", - "\n", - "classifier = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)\n", - "\n", - "classifier.fit(x_train, y_train, batch_size=128, nb_epochs=10)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 60000 samples\n", - "Epoch 1/10\n", - "60000/60000 [==============================] - 34s 560us/sample - loss: 0.2205 - accuracy: 0.9340\n", - "Epoch 2/10\n", - "60000/60000 [==============================] - 4s 71us/sample - loss: 0.0624 - accuracy: 0.9811\n", - "Epoch 3/10\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0456 - accuracy: 0.9858\n", - "Epoch 4/10\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0365 - accuracy: 0.9889\n", - "Epoch 5/10\n", - "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0294 - accuracy: 0.9906\n", - "Epoch 6/10\n", - "60000/60000 [==============================] - 4s 71us/sample - loss: 0.0257 - accuracy: 0.9919\n", - "Epoch 7/10\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0213 - accuracy: 0.9930\n", - "Epoch 8/10\n", - "60000/60000 [==============================] - 4s 71us/sample - loss: 0.0194 - accuracy: 0.9938\n", - "Epoch 9/10\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0166 - accuracy: 0.9946\n", - "Epoch 10/10\n", - "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0150 - accuracy: 0.9948\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "HoS7HK-8ATSa", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "60b39828-547e-445a-cb3f-ff428be0a52e" - }, - "source": [ - "predictions_test = classifier.predict(x_test)\n", - "accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n", - "print(\"Accuracy on test examples: {:.2f}%\".format(accuracy * 100))" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy on test examples: 99.21%\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4mFwoBCTIW3e" - }, - "source": [ - "# Ataque de inversión" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Mb0xkD6DIS6a" - }, - "source": [ - "from art.attacks.inference.model_inversion import MIFace" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "JqiP7TsAIdnY" - }, - "source": [ - "y = np.arange(10)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "Cl9_FpBzIk8y" - }, - "source": [ - "attack = MIFace(classifier, max_iter=10000, threshold=0.99)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "z3pIdGU1Io09" - }, - "source": [ - "x_init_white = np.zeros((10, 28, 28, 1))\n", - "x_init_grey = np.zeros((10, 28, 28, 1)) + 0.5\n", - "x_init_black = np.ones((10, 28, 28, 1))\n", - "x_init_random = np.random.uniform(0, 1, (10, 28, 28, 1))\n", - "x_init_average = np.zeros((10, 28, 28, 1)) + np.mean(x_test, axis=0)\n", - "\n", - "x_inits = [x_init_white, x_init_grey, x_init_black, x_init_random, x_init_average]\n", - "x_inits_names = [\"white\", \"grey\", \"black\", \"random\", \"average\"]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "WDZgf53CgrRg", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 177, - "referenced_widgets": [ - "098b465599294dcdb09822221146858a", - "a51d958807604e319d82058484bf83c9", - "595cf57b4fb148b18d61bacb5ac872b1", - "295d439b06ed4c839bf3c7e7ffd47f39", - "4e6bacd1a7bb4de49ea01cc12b3beb61", - "3cdae3dd806341f3bb3b87d1b5b6ef83", - "5f61b6ed508e4bec8bff4984b2583473", - "1adb642ae4944f9d8d79d92a07859bda", - "13ee1969a494471facff9a2420e3a065", - "6d8d12c5549e456e976c5f078b455b9b", - "e950fdae829e48aa92993aec9e36e96a", - "d9504d4d140944dfa05c8865a94c6ece", - "189df82091da4214957a29d9f646045e", - "2fd72e82ba94401684d6e8410bf85537", - "2dca1f69b5f2424b97e30244733e873b", - "cb13366ed4614cf5ac4a77bb69aba366", - "a939f83e2b4841c98fc4ea2a47e979a2", - "7ed9ef0b2781419e9325a23808c890e3", - "0e132f91168f4d2e8782c885d5024b11", - "9364974ecac3429d999fb89dec55acea", - "72436df9d0f849db9889ad2658eb9949", - "0e1f1dfc8f2d44fcac949e4d54808978", - "d958964cdc5a4f1788333781fd9b25e8", - "b909085196744cb3a6694ea6816b33b3", - "816bae0eaabe4410a7de802bdba74e47", - "60b5be28ed014573983bb072dc46141d", - "f8450bb0a88346849aeff9dc7d69ab23", - "f8e12c24e0864d1683697c454b89554c", - "d7fac88220bb44e7bfef7b59ebd5603b", - "1a9674c73ad94338a674c6dc93d7aac2", - "eda59d8fe6ce4d6d98c4a4c9de68f478", - "a0dec1d25aa64bdf8a9adaa050099aca", - "960094ce0e6b438ab525ba08c842c4b7", - "ef7ac04494c74e28b15689311484a0fe", - "3cb4b6065c4946cbb9530c95c18258a2", - "3a56e305d33940ff96df571e458c6ac6", - "b30b6aa5ec374525b1c3809db2a3c1a6", - "6db3268f471a4f34b6558d606dd4330f", - "efbe070241d34c29951f311427c3faa4", - "972370b629c941a48934281f1d7a72b8", - "5868261019bc41f682571f624e2d2fc9", - "07742f38accc4df2a9110cd1065ef024", - "0ad791b9983846eab4e691ce5386b574", - "365006486fd7427589cd13a1d2076a34", - "2dbc957bc0d5443d94671ec3b40bd988", - "b5551a808b2848368b8a4defdf3f8731", - "2273824f47e8439a82ebbb52e02bc2b6", - "af27778a11c244d1940bd9c3bc90e656", - "bc0f3ca6d6234694995a26472a8899aa", - "4fb2849859b24e91987c6080ce93b7f7", - "090a691352cd42f1b57129ccee3edd0c", - "018de9b3e72647ad95a9a7a075a53055", - "133bc1e7c8f74b2a877fac6d486a2576", - "7ec315ed80e244b09d1cd7a4f111fe0c", - "3ff84f41bdc7499bba2d7e079a812358" - ] - }, - "outputId": "47798e0a-0fda-4fef-86fb-05569de7bc8c" - }, - "source": [ - "x_infers = []\n", - "for x in x_inits:\n", - " x_infers.append(attack.infer(x, y))" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "098b465599294dcdb09822221146858a", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Model inversion: 0%| | 0/10 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Z3GsUwx6rsvI" - }, - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "_YsUTdj30erI", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "e842f2ea-6cde-461a-a079-3b552686f856" - }, - "source": [ - "fig = plt.figure(figsize=(10, 20))\n", - "outer = gridspec.GridSpec(5, 1, hspace=0.5)\n", - "\n", - "for i, x_infer in enumerate(x_infers):\n", - " inner = gridspec.GridSpecFromSubplotSpec(2, 5, subplot_spec=outer[i], wspace=0.1, hspace=0.1)\n", - "\n", - " for j in range(10):\n", - " ax = plt.Subplot(fig, inner[j])\n", - " ax.imshow(x_infers[i][j].reshape((28, 28)), cmap='gray_r')\n", - " \n", - " if j == 2:\n", - " ax.set_title(x_inits_names[i])\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " fig.add_subplot(ax)\n", - "\n", - "fig.show()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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Adversarial Robustness Toolkit (ART)\n","\n","* Documentación: https://adversarial-robustness-toolbox.readthedocs.io/en/latest/\n","* Código: https://github.com/Trusted-AI/adversarial-robustness-toolbox\n","* Ejemplos: https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/examples"]},{"cell_type":"markdown","metadata":{"id":"NuoZ5bhE-2_0"},"source":["## Instalación"]},{"cell_type":"code","metadata":{"id":"Zua3aciV-O5K","colab":{"base_uri":"https://localhost:8080/"},"outputId":"49165629-3eda-4fd7-b7be-d5e5f90a579c","executionInfo":{"status":"ok","timestamp":1660403911436,"user_tz":-120,"elapsed":9111,"user":{"displayName":"","userId":""}}},"source":["!pip install adversarial-robustness-toolbox==1.11.0"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting adversarial-robustness-toolbox==1.11.0\n"," Downloading adversarial_robustness_toolbox-1.11.0-py3-none-any.whl (1.3 MB)\n","\u001b[K |████████████████████████████████| 1.3 MB 7.7 MB/s \n","\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (4.64.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (57.4.0)\n","Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.21.6)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.7.3)\n","Requirement already satisfied: numba>=0.53.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (0.56.0)\n","Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.0.2)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.15.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.12.0)\n","Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (0.39.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (1.1.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (3.1.0)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (3.8.1)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.1.1)\n","Installing collected packages: adversarial-robustness-toolbox\n","Successfully installed adversarial-robustness-toolbox-1.11.0\n"]}]},{"cell_type":"code","metadata":{"id":"1M8cmX6Y_MaU","executionInfo":{"status":"ok","timestamp":1660403932832,"user_tz":-120,"elapsed":16243,"user":{"displayName":"","userId":""}}},"source":["import keras\n","from keras.models import Sequential\n","from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from art.utils import load_mnist"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"3JSlG18GC1RT","executionInfo":{"status":"ok","timestamp":1660403932846,"user_tz":-120,"elapsed":353,"user":{"displayName":"","userId":""}}},"source":["import tensorflow as tf\n","tf.compat.v1.disable_eager_execution()\n","\n","import warnings\n","warnings.filterwarnings('ignore')"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"6k-Q4bnG_SWG","executionInfo":{"status":"ok","timestamp":1660403932865,"user_tz":-120,"elapsed":359,"user":{"displayName":"","userId":""}}},"source":["%matplotlib inline"],"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rYWQUSRC_WLT"},"source":["## Cargar datos"]},{"cell_type":"code","metadata":{"id":"hq1wjOgu_a1U","executionInfo":{"status":"ok","timestamp":1660403932870,"user_tz":-120,"elapsed":337,"user":{"displayName":"","userId":""}}},"source":["(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gGp89T6o_s3M"},"source":["## Entrenar modelo"]},{"cell_type":"code","metadata":{"id":"gTOks8pDAObr","executionInfo":{"status":"ok","timestamp":1660403939210,"user_tz":-120,"elapsed":18,"user":{"displayName":"","userId":""}}},"source":["from art.estimators.classification import KerasClassifier"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"IwvRBqtoARMM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"48b20fe9-5aed-40e7-f034-e1b87bd762c6","executionInfo":{"status":"ok","timestamp":1660403977868,"user_tz":-120,"elapsed":35931,"user":{"displayName":"","userId":""}}},"source":["model = Sequential()\n","model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Conv2D(64, (3, 3), activation='relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Dropout(0.25))\n","model.add(Flatten())\n","model.add(Dense(128, activation='relu'))\n","model.add(Dense(10, activation='softmax'))\n","\n","model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n","\n","classifier = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)\n","\n","classifier.fit(x_train, y_train, batch_size=128, nb_epochs=10)"],"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Train on 60000 samples\n","Epoch 1/10\n","60000/60000 [==============================] - 11s 187us/sample - loss: 0.2209 - accuracy: 0.9344\n","Epoch 2/10\n","60000/60000 [==============================] - 2s 38us/sample - loss: 0.0671 - accuracy: 0.9797\n","Epoch 3/10\n","60000/60000 [==============================] - 2s 38us/sample - loss: 0.0474 - accuracy: 0.9852\n","Epoch 4/10\n","60000/60000 [==============================] - 2s 37us/sample - loss: 0.0380 - accuracy: 0.9884\n","Epoch 5/10\n","60000/60000 [==============================] - 2s 37us/sample - loss: 0.0306 - accuracy: 0.9901\n","Epoch 6/10\n","60000/60000 [==============================] - 2s 37us/sample - loss: 0.0269 - accuracy: 0.9917\n","Epoch 7/10\n","60000/60000 [==============================] - 2s 37us/sample - loss: 0.0227 - accuracy: 0.9928\n","Epoch 8/10\n","60000/60000 [==============================] - 2s 37us/sample - loss: 0.0195 - accuracy: 0.9938\n","Epoch 9/10\n","60000/60000 [==============================] - 2s 38us/sample - loss: 0.0169 - accuracy: 0.9941\n","Epoch 10/10\n","60000/60000 [==============================] - 2s 38us/sample - loss: 0.0141 - accuracy: 0.9956\n"]}]},{"cell_type":"code","metadata":{"id":"HoS7HK-8ATSa","colab":{"base_uri":"https://localhost:8080/"},"outputId":"667fd425-d372-4532-a2f9-3b2f5686027d","executionInfo":{"status":"ok","timestamp":1660403987199,"user_tz":-120,"elapsed":837,"user":{"displayName":"","userId":""}}},"source":["predictions_test = classifier.predict(x_test)\n","accuracy = np.sum(np.argmax(predictions_test, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n","print(\"Accuracy on test examples: {:.2f}%\".format(accuracy * 100))"],"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on test examples: 99.26%\n"]}]},{"cell_type":"markdown","metadata":{"id":"4mFwoBCTIW3e"},"source":["# Ataque de inversión"]},{"cell_type":"code","metadata":{"id":"Mb0xkD6DIS6a","executionInfo":{"status":"ok","timestamp":1660403999661,"user_tz":-120,"elapsed":589,"user":{"displayName":"","userId":""}}},"source":["from art.attacks.inference.model_inversion import MIFace"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"JqiP7TsAIdnY","executionInfo":{"status":"ok","timestamp":1660404004030,"user_tz":-120,"elapsed":57,"user":{"displayName":"","userId":""}}},"source":["y = np.arange(10)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"Cl9_FpBzIk8y","executionInfo":{"status":"ok","timestamp":1660404006588,"user_tz":-120,"elapsed":26,"user":{"displayName":"","userId":""}}},"source":["attack = MIFace(classifier, max_iter=10000, threshold=0.99)"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"z3pIdGU1Io09","executionInfo":{"status":"ok","timestamp":1660404010836,"user_tz":-120,"elapsed":23,"user":{"displayName":"","userId":""}}},"source":["x_init_white = np.zeros((10, 28, 28, 1))\n","x_init_grey = np.zeros((10, 28, 28, 1)) + 0.5\n","x_init_black = np.ones((10, 28, 28, 1))\n","x_init_random = np.random.uniform(0, 1, (10, 28, 28, 1))\n","x_init_average = np.zeros((10, 28, 28, 1)) + np.mean(x_test, axis=0)\n","\n","x_inits = [x_init_white, x_init_grey, x_init_black, x_init_random, x_init_average]\n","x_inits_names = [\"white\", \"grey\", \"black\", \"random\", \"average\"]"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"WDZgf53CgrRg","colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["3b5c00c932884accaa5be65078953bff","1aac291090cf4e4c868e346955d05d27","7aa675cb195d463baf772478cb858f82","04c8eef5f17f4d3d847e93a2652d2da4","03a21c230e804b93974b91576dc487d0","e509624fa1cb4a7ba2f3ddb005921450","c805fcc39b294d95adf95278d3a7b8b9","77897269ef724d17a3986de9c5b277a2","5e2612aaab234815ad0afa0747bc08c8","e764ef2d0d2c4f9db2733e289725de14","013774c368424b6a90d44fdaa442695a","cb8e6d3c28204b8690b9db59cc457704","a95a7520bfa5458ba89016ee225f9fcb","c1773dd458e94e4f817a9e39027772ec","06b12bae1b344d65b1ecfa450bb095a9","c3fcbd170d834ddeb64d0d1542aa5426","0c3a85d1ead6428d988eeae7ca9c78db","ba8b4ca8ae7b4a6ebad98abb72007f2f","685f8d114eba4421ae0e579d65fb4573","a5e8115046cb440584667d3a7f28ea56","217e2934f64241c68ab36e7380c38df4","00cf4f0954bf4d5286644ad21ad42553","2a8b05cd1878456687b11d7e7061a270","65c41212c92543419abc551e32e326c6","bf34dab1b54d4120aa39366c3308ddca","c30a640248de4980bb2ef53fdcf47645","e415a08d28354746b05c623e040a2244","7a4ffa25bd22409f97c8575f7e119dc3","e6da5f1828d54bc7b86a3661058aa21d","afb2fb5902b245aeb6fc4fa96a3f083f","ad556caff0964b98bfc945497b4f494a","9a8629b695ee4492b31e02dc94a68c5c","ed9e2abf8f324fa18e48deb83e46e849","8d97ce4ab6e4418cbb3be51b799b2188","1533650fb27c4d0bafc4eaec519ae6fc","239600a8262d48b0b25f7ac4ad45222b","0c7f4e92810c42419a71cae5873c335c","9af10909a38b467bb7e25f402a157c66","f4062a12646f4d75a87a75bf2f3f95ac","f43e36e7e6bf4d5ab108ff4a16fab8bd","a571835ccb904cabb0acd5ef69134854","f612b7009ea240fcb2e324f07cd5069f","862412552aa64782bec207d7a41a4c80","6cbbdb38e0b44169ba940c6c1c1b5b27","da3f5eec823e4734986afb83f3cace55","d14653ceb3ea4ef2b00b532568075e7f","9050a43a6797451b9701c9b83a5c7ba4","e0d85b34ad9e409b8a03738926c42ede","9eb18b07a0944bb2b1130b1076cb14fb","ce7659d7267a4b79862bb8c003a3d8b5","763838e15d4c4fa49ee48a2ef2a98bc3","39aaff85374f4d3aa8d97cd02699b2d3","52ff32ea481547e6bb41e50efbb47019","1efab9dfcb304d0585acf595f592c648","cf734f753fa24c81a4c61a69f1692b86"]},"outputId":"81ccaa77-dd3b-42f3-fadc-632eaef75318","executionInfo":{"status":"ok","timestamp":1660405814899,"user_tz":-120,"elapsed":1796420,"user":{"displayName":"","userId":""}}},"source":["x_infers = []\n","for x in x_inits:\n"," x_infers.append(attack.infer(x, y))"],"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":["Model inversion: 0%| | 0/10 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Z3GsUwx6rsvI","executionInfo":{"status":"ok","timestamp":1660405852241,"user_tz":-120,"elapsed":502,"user":{"displayName":"","userId":""}}},"source":["import matplotlib.pyplot as plt\n","import matplotlib.gridspec as gridspec"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"id":"_YsUTdj30erI","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"2131ed8c-ad7f-4cc0-941a-a349f6ae7e77","executionInfo":{"status":"ok","timestamp":1660405856766,"user_tz":-120,"elapsed":2903,"user":{"displayName":"","userId":""}}},"source":["fig = plt.figure(figsize=(10, 20))\n","outer = gridspec.GridSpec(5, 1, hspace=0.5)\n","\n","for i, x_infer in enumerate(x_infers):\n"," inner = gridspec.GridSpecFromSubplotSpec(2, 5, subplot_spec=outer[i], wspace=0.1, hspace=0.1)\n","\n"," for j in range(10):\n"," ax = plt.Subplot(fig, inner[j])\n"," ax.imshow(x_infers[i][j].reshape((28, 28)), cmap='gray_r')\n"," \n"," if j == 2:\n"," ax.set_title(x_inits_names[i])\n"," ax.set_xticks([])\n"," ax.set_yticks([])\n"," fig.add_subplot(ax)\n","\n","fig.show()"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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"cell_type": "markdown", - "metadata": { - "id": "CLrkyRc6-rD5" - }, - "source": [ - "# Adversarial Robustness Toolkit\n", - "\n", - "* Documentación: https://adversarial-robustness-toolbox.readthedocs.io/en/latest/\n", - "* Código: https://github.com/Trusted-AI/adversarial-robustness-toolbox\n", - "* Ejemplos: https://github.com/Trusted-AI/adversarial-robustness-toolbox/tree/main/examples" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NuoZ5bhE-2_0" - }, - "source": [ - "## Instalación" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "dW8FLnz3SOT_", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "31fac04c-3f1d-4361-f4b5-5e5f5667374d" - }, - "source": [ - "!pip install adversarial-robustness-toolbox==1.8.1" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting adversarial-robustness-toolbox==1.8.1\n", - " Downloading adversarial_robustness_toolbox-1.8.1-py3-none-any.whl (1.1 MB)\n", - "\u001b[K |████████████████████████████████| 1.1 MB 5.2 MB/s \n", - "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (4.62.3)\n", - "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (57.4.0)\n", - "Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.0.1)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.15.0)\n", - "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.8.1) (1.4.1)\n", - "Collecting numba>=0.53.1\n", - " Downloading numba-0.54.1-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (3.3 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llvmlite-0.34.0:\n", - " Successfully uninstalled llvmlite-0.34.0\n", - " Attempting uninstall: numba\n", - " Found existing installation: numba 0.51.2\n", - " Uninstalling numba-0.51.2:\n", - " Successfully uninstalled numba-0.51.2\n", - "Successfully installed adversarial-robustness-toolbox-1.8.1 llvmlite-0.37.0 numba-0.54.1\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1M8cmX6Y_MaU" - }, - "source": [ - "import keras.backend as k\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from art.utils import load_mnist, preprocess" - ], - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "6k-Q4bnG_SWG" - }, - "source": [ - "%matplotlib inline" - ], - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "lepJHgCzUojl" - }, - "source": [ - "import tensorflow as tf\n", - "tf.compat.v1.disable_eager_execution()\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ], - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "cHF76IhPRuCs" - }, - "source": [ - "from art.attacks.poisoning import PoisoningAttackBackdoor\n", - "from art.attacks.poisoning.perturbations import add_pattern_bd\n", - "from art.utils import preprocess" - ], - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "6bn5GVCuV_S8" - }, - "source": [ - "from art.defences.transformer.poisoning import NeuralCleanse" - ], - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYWQUSRC_WLT" - }, - "source": [ - "## Ataque de envenenamiento\n", - "\n", - "> Ejemplo tomado de\n", - "> https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_defense_neural_cleanse.ipynb" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hq1wjOgu_a1U" - }, - "source": [ - "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n", - "\n", - "n_train = np.shape(x_raw)[0]\n", - "num_selection = 7500\n", - "random_selection_indices = np.random.choice(n_train, num_selection)\n", - "x_raw = x_raw[random_selection_indices]\n", - "y_raw = y_raw[random_selection_indices]" - ], - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "JBhvuz68QRDm" - }, - "source": [ - "max_val = np.max(x_raw)\n", - "def poison_func(x):\n", - " return add_pattern_bd(x, pixel_value=max_val)" - ], - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "vV_pIEkOQVJG" - }, - "source": [ - "def poison_dataset(x_clean, y_clean, percent_poison, poison_func):\n", - " x_poison = np.copy(x_clean)\n", - " y_poison = np.copy(y_clean)\n", - " is_poison = np.zeros(np.shape(y_poison))\n", - " \n", - " sources = np.arange(10) # 0, 1, 2, 3, ...\n", - " targets = (np.arange(10) + 1) % 10 # 1, 2, 3, 4, ...\n", - " for i, (src, tgt) in enumerate(zip(sources, targets)):\n", - " n_points_in_tgt = np.size(np.where(y_clean == tgt))\n", - " num_poison = round((percent_poison * n_points_in_tgt) / (1 - percent_poison))\n", - " src_imgs = x_clean[y_clean == src]\n", - "\n", - " n_points_in_src = np.shape(src_imgs)[0]\n", - " indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)\n", - "\n", - " imgs_to_be_poisoned = np.copy(src_imgs[indices_to_be_poisoned])\n", - " backdoor_attack = PoisoningAttackBackdoor(poison_func)\n", - " imgs_to_be_poisoned, poison_labels = backdoor_attack.poison(imgs_to_be_poisoned, y=np.ones(num_poison) * tgt)\n", - " x_poison = np.append(x_poison, imgs_to_be_poisoned, axis=0)\n", - " y_poison = np.append(y_poison, poison_labels, axis=0)\n", - " is_poison = np.append(is_poison, np.ones(num_poison))\n", - "\n", - " is_poison = is_poison != 0\n", - " return is_poison, x_poison, y_poison" - ], - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "io8YPoQuQW-7" - }, - "source": [ - "# Poison training data\n", - "percent_poison = 0.33\n", - "(is_poison_train, x_poisoned_raw, y_poisoned_raw) = poison_dataset(x_raw, y_raw, percent_poison, poison_func)\n", - "x_train, y_train = preprocess(x_poisoned_raw, y_poisoned_raw)\n", - "# Add channel axis:\n", - "x_train = np.expand_dims(x_train, axis=3)\n", - "\n", - "# Poison test data\n", - "(is_poison_test, x_poisoned_raw_test, y_poisoned_raw_test) = poison_dataset(x_raw_test, y_raw_test, percent_poison, poison_func)\n", - "x_test, y_test = preprocess(x_poisoned_raw_test, y_poisoned_raw_test)\n", - "# Add channel axis:\n", - "x_test = np.expand_dims(x_test, axis=3)\n", - "\n", - "# Shuffle training data\n", - "n_train = np.shape(y_train)[0]\n", - "shuffled_indices = np.arange(n_train)\n", - "np.random.shuffle(shuffled_indices)\n", - "x_train = x_train[shuffled_indices]\n", - "y_train = y_train[shuffled_indices]" - ], - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gGp89T6o_s3M" - }, - "source": [ - "## Entrenar modelo" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "gTOks8pDAObr" - }, - "source": [ - "from art.estimators.classification import KerasClassifier" - ], - "execution_count": 11, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "IwvRBqtoARMM", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5edcf0ff-682e-4ceb-d22a-bf543afdfd70" - }, - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=x_train.shape[1:]))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(10, activation='softmax'))\n", - "\n", - "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", - "\n", - "classifier = KerasClassifier(model=model, clip_values=(min_, max_))\n", - "classifier.fit(x_train, y_train, nb_epochs=3, batch_size=128)" - ], - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train on 11193 samples\n", - "Epoch 1/3\n", - "11193/11193 [==============================] - 26s 2ms/sample - loss: 1.2868 - accuracy: 0.5444\n", - "Epoch 2/3\n", - "11193/11193 [==============================] - 26s 2ms/sample - loss: 0.4713 - accuracy: 0.8529\n", - "Epoch 3/3\n", - "11193/11193 [==============================] - 28s 3ms/sample - loss: 0.2887 - accuracy: 0.9145\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "HoS7HK-8ATSa", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4ce897cd-04e4-44e4-daf8-19ac7f05b9fe" - }, - "source": [ - "clean_x_test = x_test[is_poison_test == 0]\n", - "clean_y_test = y_test[is_poison_test == 0]\n", - "\n", - "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n", - "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n", - "clean_total = clean_y_test.shape[0]\n", - "\n", - "clean_acc = clean_correct / clean_total\n", - "print(\"Clean test set accuracy: {:.2f}%\".format(clean_acc * 100))\n", - "\n", - "poison_x_test = x_test[is_poison_test]\n", - "poison_y_test = y_test[is_poison_test]\n", - "\n", - "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n", - "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n", - "poison_total = poison_y_test.shape[0]\n", - "\n", - "poison_acc = poison_correct / poison_total\n", - "print(\"Effectiveness of poison: {:.2f}%\".format(poison_acc * 100))" - ], - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Clean test set accuracy: 96.76%\n", - "Effectiveness of poison: 94.80%\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "k-70ggKKbr-d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 301 - }, - "outputId": "96d8d685-66ef-4a7a-d74e-bca12ae4eceb" - }, - "source": [ - "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n", - "\n", - "c = 1 # class to display\n", - "i = 6 # image of the class to display\n", - "\n", - "c_idx = np.where(np.argmax(clean_y_test,1) == c)[0][i] # index of the image in clean arrays\n", - "c_idx_p = np.where(np.argmax(poison_y_test,1) == c)[0][i] # index of the image in poison arrays\n", - "\n", - "fig = plt.figure(figsize=(10, 5))\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "ax.imshow(clean_x_test[c_idx].reshape((28, 28)), cmap=\"gray\")\n", - "ax.axis('off')\n", - "ax.set_title(\"Prediction: {}\".format(clean_preds[c_idx]))\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "ax.imshow(poison_x_test[c_idx_p].reshape((28, 28)), cmap=\"gray\")\n", - "ax.axis('off')\n", - "ax.set_title(\"Prediction: {}\".format(poison_preds[c_idx_p]))\n", - "plt.show()" - ], - "execution_count": 17, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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AAOkpPABAegoPAJCewgMApKfwAADpOXgQABizkzkM8FTcpxs7PABAegoPAJCewgMApKfwAADpKTwAQHoKDwCQnsIDAKSn8AAA6ZVa63jPAABwStnhAQDSU3gAgPQUHgAgPYUHAEhP4QEA0lN4AID0/g8KOa7M5msPbAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BAVEnwbcOy11" - }, - "source": [ - "## Defensa - Neural Cleanse\n", - "\n", - "### Detectar puerta trasera" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "m0kI4hgdOyJZ" - }, - "source": [ - "cleanse = NeuralCleanse(classifier)\n", - "defence_cleanse = cleanse(classifier, steps=10, learning_rate=0.1)" - ], - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "tKWWA1fEQ7aU", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 314, - "referenced_widgets": [ - "3fe2dc14cadd40d2a00f1f02c747eec1", - "1faea808fa7b476e9c6b92b931461aff", - "dfbbc8cf699e4d2c8e062c38829686bc", - "d3dde776f4734a7ea04c0386be29a0d6", - "334b518a24d84f14834153dbdc4833f6", - "e3bec477fd7b462d9cad252ce4580444", - "4f530a7d897f40f0a77869e1b12727aa", - "48e768e75e3240d2ba1f4e1f27139c71", - "6de355b8cfe24e95a1a0c2870fea1d49", - "028f0df6b2bd43f883241352f63f73c8", - "a0e4f04e6bf24d2fb3c79678ac203aaa" - ] - }, - "outputId": "e19d71ec-7436-4cdb-cc52-f624de55395d" - }, - "source": [ - "pattern, mask = defence_cleanse.generate_backdoor(x_test, y_test, np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0]))\n", - "plt.imshow(np.squeeze(mask * pattern))" - ], - "execution_count": 19, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3fe2dc14cadd40d2a00f1f02c747eec1", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Generating backdoor for class 1: 0%| | 0/10 [00:00" - ] - }, - "metadata": {}, - "execution_count": 19 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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"09ea9c09d0ff433292af131317c6f486", - "35036e01be4b4f5db16c0826a5bd93bb", - "2b246ed6f4b848cd9bc1473ce989ed95", - "911c5a6bd159482a9c5c6350940da559", - "875b49a97e0042c69fef19c1b0df6797", - "b9dd62e7428a45728a8ffc5877462e07", - "da59f01f8b784260b36c1f4214369ef6", - "88f0ffef50f8494c955b9432f5b1e28f", - "7614ca1de57c40b5bf907fa2d717d1e6", - "124142f5672e41c78d1ded9e5a7d5345", - "20b396158e234ba485371ca7a693f36d", - "40870c02c35a401bbea53d6514081b4e", - "86782699357d4267b5ffce3a564912ba", - "4f05adc9590d4836b8b5242ccfd76291", - "6a56e562706e458f8e9b4141a287131b", - "0f017ba062944129ae5b7cdd8f61b3ce", - "cf0a032f30c14a4086262df342382cdc", - "0ee106f3ae3e48cb9f3140db1c4f8be4", - "810d1c89b6d14512937962ce250308c4", - "c4a95554c12842a7b4392369c9a6ae70", - "143ff020b4c748e9a6bb2f4c496333c3", - "f2a5ef2c20c74689b9f6ae61528792e1", - "354cbe21d48a46afa4edcbe2660ff645", - "2d494323e08840f39ceacd5366200292", - "ba4cb41a49de49dcbbc675c5647a6b09", - "2eb11dbbd2294f01b347cc4f451a9954", - "faaea6442987401486f134fa57e10dad", - "0f4b12f6f860463ca144fab949190201", - "2820daf20eec49e58ab91dd04a39534a", - "e9c39f0bf3a949ad93c479eda9436037", - "381b95b4e82a4bbb99d369bee7d7b55c", - "24fca55437994652b196c35f5a499275", - "57ecece1e64c4a3db414f01191240390", - "1d4d92ca3de84e7784f50a735fd5e15f", - "82ad9f177504472697b4d303519d64ad", - "7fd2185f9e9a4843a1d1f3b31639eea2", - "d950c4a7662143eba0401ff8d2d5c514", - "05f1db98bdff4cd3b50e65dc6f159e82", - "b3f4a5b89d204e8ebafc2ed88777f503", - "59cf0ad3cbb347549bc3767c0b9ff81a", - "806ab8cef0cf4a81a655711611341cc8" - ] - }, - "outputId": "cb16932f-d3d2-469a-c360-9f23fbf9390b" - }, - "source": [ - "defence_cleanse = cleanse(classifier, steps=10, learning_rate=0.1)\n", - "defence_cleanse.mitigate(clean_x_test, clean_y_test, mitigation_types=[\"filtering\"])" - ], - "execution_count": 20, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6f0fab92427d479e99d2c4a1569ceed6", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Generating backdoor for class 0: 0%| | 0/10 [00:00=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.21.6)\n","Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.0.2)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (57.4.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.15.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (4.64.0)\n","Requirement already satisfied: numba>=0.53.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (0.56.0)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.11.0) (1.7.3)\n","Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (0.39.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.12.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (3.1.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox==1.11.0) (1.1.0)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (4.1.1)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.53.1->adversarial-robustness-toolbox==1.11.0) (3.8.1)\n","Installing collected packages: adversarial-robustness-toolbox\n","Successfully installed adversarial-robustness-toolbox-1.11.0\n"]}]},{"cell_type":"code","metadata":{"id":"1M8cmX6Y_MaU","executionInfo":{"status":"ok","timestamp":1660406149162,"user_tz":-120,"elapsed":12663,"user":{"displayName":"","userId":""}}},"source":["import keras.backend as k\n","from keras.models import Sequential\n","from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","from art.utils import load_mnist, preprocess"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"6k-Q4bnG_SWG","executionInfo":{"status":"ok","timestamp":1660406149166,"user_tz":-120,"elapsed":62,"user":{"displayName":"","userId":""}}},"source":["%matplotlib inline"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"lepJHgCzUojl","executionInfo":{"status":"ok","timestamp":1660406149171,"user_tz":-120,"elapsed":57,"user":{"displayName":"","userId":""}}},"source":["import tensorflow as tf\n","tf.compat.v1.disable_eager_execution()\n","\n","import warnings\n","warnings.filterwarnings('ignore')"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"cHF76IhPRuCs","executionInfo":{"status":"ok","timestamp":1660406149175,"user_tz":-120,"elapsed":55,"user":{"displayName":"","userId":""}}},"source":["from art.attacks.poisoning import PoisoningAttackBackdoor\n","from art.attacks.poisoning.perturbations import add_pattern_bd\n","from art.utils import preprocess"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"6bn5GVCuV_S8","executionInfo":{"status":"ok","timestamp":1660406150335,"user_tz":-120,"elapsed":26,"user":{"displayName":"","userId":""}}},"source":["from art.defences.transformer.poisoning import NeuralCleanse"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rYWQUSRC_WLT"},"source":["## Ataque de envenenamiento\n","\n","> Ejemplo tomado de\n","> https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_defense_neural_cleanse.ipynb"]},{"cell_type":"code","metadata":{"id":"hq1wjOgu_a1U","executionInfo":{"status":"ok","timestamp":1660406154369,"user_tz":-120,"elapsed":1243,"user":{"displayName":"","userId":""}}},"source":["(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n","\n","n_train = np.shape(x_raw)[0]\n","num_selection = 7500\n","random_selection_indices = np.random.choice(n_train, num_selection)\n","x_raw = x_raw[random_selection_indices]\n","y_raw = y_raw[random_selection_indices]"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"JBhvuz68QRDm","executionInfo":{"status":"ok","timestamp":1660406157118,"user_tz":-120,"elapsed":395,"user":{"displayName":"","userId":""}}},"source":["max_val = np.max(x_raw)\n","def poison_func(x):\n"," return add_pattern_bd(x, pixel_value=max_val)"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"vV_pIEkOQVJG","executionInfo":{"status":"ok","timestamp":1660406158871,"user_tz":-120,"elapsed":17,"user":{"displayName":"","userId":""}}},"source":["def poison_dataset(x_clean, y_clean, percent_poison, poison_func):\n"," x_poison = np.copy(x_clean)\n"," y_poison = np.copy(y_clean)\n"," is_poison = np.zeros(np.shape(y_poison))\n"," \n"," sources = np.arange(10) # 0, 1, 2, 3, ...\n"," targets = (np.arange(10) + 1) % 10 # 1, 2, 3, 4, ...\n"," for i, (src, tgt) in enumerate(zip(sources, targets)):\n"," n_points_in_tgt = np.size(np.where(y_clean == tgt))\n"," num_poison = round((percent_poison * n_points_in_tgt) / (1 - percent_poison))\n"," src_imgs = x_clean[y_clean == src]\n","\n"," n_points_in_src = np.shape(src_imgs)[0]\n"," indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)\n","\n"," imgs_to_be_poisoned = np.copy(src_imgs[indices_to_be_poisoned])\n"," backdoor_attack = PoisoningAttackBackdoor(poison_func)\n"," imgs_to_be_poisoned, poison_labels = backdoor_attack.poison(imgs_to_be_poisoned, y=np.ones(num_poison) * tgt)\n"," x_poison = np.append(x_poison, imgs_to_be_poisoned, axis=0)\n"," y_poison = np.append(y_poison, poison_labels, axis=0)\n"," is_poison = np.append(is_poison, np.ones(num_poison))\n","\n"," is_poison = is_poison != 0\n"," return is_poison, x_poison, y_poison"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"io8YPoQuQW-7","executionInfo":{"status":"ok","timestamp":1660406165290,"user_tz":-120,"elapsed":377,"user":{"displayName":"","userId":""}}},"source":["# Poison training data\n","percent_poison = 0.33\n","(is_poison_train, x_poisoned_raw, y_poisoned_raw) = poison_dataset(x_raw, y_raw, percent_poison, poison_func)\n","x_train, y_train = preprocess(x_poisoned_raw, y_poisoned_raw)\n","# Add channel axis:\n","x_train = np.expand_dims(x_train, axis=3)\n","\n","# Poison test data\n","(is_poison_test, x_poisoned_raw_test, y_poisoned_raw_test) = poison_dataset(x_raw_test, y_raw_test, percent_poison, poison_func)\n","x_test, y_test = preprocess(x_poisoned_raw_test, y_poisoned_raw_test)\n","# Add channel axis:\n","x_test = np.expand_dims(x_test, axis=3)\n","\n","# Shuffle training data\n","n_train = np.shape(y_train)[0]\n","shuffled_indices = np.arange(n_train)\n","np.random.shuffle(shuffled_indices)\n","x_train = x_train[shuffled_indices]\n","y_train = y_train[shuffled_indices]"],"execution_count":10,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gGp89T6o_s3M"},"source":["## Entrenar modelo"]},{"cell_type":"code","metadata":{"id":"gTOks8pDAObr","executionInfo":{"status":"ok","timestamp":1660406170430,"user_tz":-120,"elapsed":878,"user":{"displayName":"","userId":""}}},"source":["from art.estimators.classification import KerasClassifier"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"IwvRBqtoARMM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0a5e2812-955d-4545-cba3-9e8c964fce56","executionInfo":{"status":"ok","timestamp":1660406257253,"user_tz":-120,"elapsed":86370,"user":{"displayName":"","userId":""}}},"source":["model = Sequential()\n","model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=x_train.shape[1:]))\n","model.add(Conv2D(64, (3, 3), activation='relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Dropout(0.25))\n","model.add(Flatten())\n","model.add(Dense(128, activation='relu'))\n","model.add(Dropout(0.5))\n","model.add(Dense(10, activation='softmax'))\n","\n","model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n","\n","classifier = KerasClassifier(model=model, clip_values=(min_, max_))\n","classifier.fit(x_train, y_train, nb_epochs=3, batch_size=128)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Train on 11194 samples\n","Epoch 1/3\n","11194/11194 [==============================] - 28s 3ms/sample - loss: 1.2750 - accuracy: 0.5329\n","Epoch 2/3\n","11194/11194 [==============================] - 29s 3ms/sample - loss: 0.4739 - accuracy: 0.8477\n","Epoch 3/3\n","11194/11194 [==============================] - 28s 2ms/sample - loss: 0.2894 - accuracy: 0.9130\n"]}]},{"cell_type":"code","metadata":{"id":"HoS7HK-8ATSa","colab":{"base_uri":"https://localhost:8080/"},"outputId":"816c0e2c-c91a-486f-8785-d906708bfcc5","executionInfo":{"status":"ok","timestamp":1660406265199,"user_tz":-120,"elapsed":8041,"user":{"displayName":"","userId":""}}},"source":["clean_x_test = x_test[is_poison_test == 0]\n","clean_y_test = y_test[is_poison_test == 0]\n","\n","clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n","clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n","clean_total = clean_y_test.shape[0]\n","\n","clean_acc = clean_correct / clean_total\n","print(\"Clean test set accuracy: {:.2f}%\".format(clean_acc * 100))\n","\n","poison_x_test = x_test[is_poison_test]\n","poison_y_test = y_test[is_poison_test]\n","\n","poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n","poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n","poison_total = poison_y_test.shape[0]\n","\n","poison_acc = poison_correct / poison_total\n","print(\"Effectiveness of poison: {:.2f}%\".format(poison_acc * 100))"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Clean test set accuracy: 96.97%\n","Effectiveness of poison: 95.23%\n"]}]},{"cell_type":"code","metadata":{"id":"k-70ggKKbr-d","colab":{"base_uri":"https://localhost:8080/","height":301},"outputId":"adf24845-b61f-45c5-9e2a-b49c751d10b2","executionInfo":{"status":"ok","timestamp":1660406308435,"user_tz":-120,"elapsed":51,"user":{"displayName":"","userId":""}}},"source":["# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n","\n","c = 1 # class to display\n","i = 20 # image of the class to display\n","\n","c_idx = np.where(np.argmax(clean_y_test,1) == c)[0][i] # index of the image in clean arrays\n","c_idx_p = np.where(np.argmax(poison_y_test,1) == c)[0][i] # index of the image in poison arrays\n","\n","fig = plt.figure(figsize=(10, 5))\n","ax = fig.add_subplot(1, 2, 1)\n","ax.imshow(clean_x_test[c_idx].reshape((28, 28)), cmap=\"gray\")\n","ax.axis('off')\n","ax.set_title(\"Prediction: {}\".format(clean_preds[c_idx]))\n","ax = fig.add_subplot(1, 2, 2)\n","ax.imshow(poison_x_test[c_idx_p].reshape((28, 28)), cmap=\"gray\")\n","ax.axis('off')\n","ax.set_title(\"Prediction: {}\".format(poison_preds[c_idx_p]))\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"BAVEnwbcOy11"},"source":["## Defensa - Neural Cleanse\n","\n","### Detectar puerta trasera"]},{"cell_type":"code","metadata":{"id":"m0kI4hgdOyJZ","executionInfo":{"status":"ok","timestamp":1660406323205,"user_tz":-120,"elapsed":837,"user":{"displayName":"","userId":""}}},"source":["cleanse = NeuralCleanse(classifier)\n","defence_cleanse = cleanse(classifier, steps=10, learning_rate=0.1)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"id":"tKWWA1fEQ7aU","colab":{"base_uri":"https://localhost:8080/","height":314,"referenced_widgets":["0ad2d444f095494c948df2c02c2f0e26","49c01a95dd104a84af72d664b1ddfd4a","fda56b879f56431293f940715cbb6992","48f13c768942467790561d6433e92f41","c5c0078d88a24153b13233c2de5a73d8","ae8340ffac26400eb2c615596a418ae3","1ff21121b138411fbab3feff82b11bc2","13e1d1ba652341da9c546126007a6b64","114bedfaf98e4cdc9518a05c43f3ecd3","adab9b01c5bd40c5bc31341c61804e9d","4f052c5ae6f748b8b3db86078b49d7db"]},"outputId":"5a967113-2260-4819-e3d1-284a12c364ff","executionInfo":{"status":"ok","timestamp":1660406592672,"user_tz":-120,"elapsed":268371,"user":{"displayName":"","userId":""}}},"source":["pattern, mask = defence_cleanse.generate_backdoor(x_test, y_test, np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0]))\n","plt.imshow(np.squeeze(mask * pattern))"],"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":["Generating backdoor for class 1: 0%| | 0/10 [00:00"]},"metadata":{},"execution_count":18},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Lk9OdY2HLFPQ"},"source":["### Mitigación"]},{"cell_type":"code","metadata":{"id":"XJcTRo6KKzQD","colab":{"base_uri":"https://localhost:8080/","height":389,"referenced_widgets":["0e20b9aa718d43dd8c6dd5635a3a4aaa","c9b0600771ee4cf19cb42262861a46a5","a9281e25e1004201bcdfd8c045cd95a0","0eb205e0076a41beba7d1b33dbb3e03d","f83dc2c23d8d44a9b79fe243892e82c0","c1bdf9dbc7ad4d9dbbc33afd026306eb","44923bd14f4946378683eb433fb8e6fd","b877ceaae23e4834aecafd4fe0828648","d3bccaacf406447581b6e1a292878505","e9f5602ebe5c4e918f1de9049322ce17","d399d2d6431b43dfb70c3e3e500f4a78","4f0e1354c877468bac31ce0467e1ffa5","80ec31e1dac94290ad7424adad5e9d7c","861ca48867354c8ea2b1779cb2ea1686","8bbfbe8edc324c029b24effeeb7ea7c4","174fa4cb0ae247eea2e2a69b8988fc1b","729ec8f865184ec7933889f8ecf02a40","bd114f9644984ff689a18693ad601273","1de1a2acdf5d43f2b9a0f2892cd61819","781b1dd719384980ad537da54214f233","acab28ec0c8b44f4b4a6a02c33f7c39d","3e3da0aad02b48fa9132ac110388803c","855b29eeca09482099322129372b4622","2a81afc487e04a5a8d435c994be3fc6e","0f918d4176944ee19cc88d245bdc608c","8f87f6c21d4e4df988ee2d259d0ded4d","91b04b3045e9453a8a6b079225ec6528","37101dc8eba04685b2a5eee49d2cdd1c","538e4fc1b28d4fefa34f1444e7422963","24766873356f4d04ba3cd46d84e5946d","518f1493b77841c0a7c2afa5357021c8","6d778688a2ac42c89392905fec89e17a","717353cb584b46bab7330e64c2f9dd97","d9578fc26f0c4311b0478e34ea37ea35","62bf511331224661bcb3178009281f70","36b53b0f6948408bbc04b2023f56010c","714e96b68be94d0d9ff32db13543b4bb","9785a94953b84e18be4866916a31864a","3e67c128c9fd44fcad3682a96cd2f3fd","0453842901264e37853ed94bbdc42c0d","1ea431c039e243c996b9febfd958576d","a5e5a405ceea46639ab7af7912702ec1","7b0c04bd53dd4dc2b5687f5af2ebfc8d","f08ca22163414f5e8c58efbec436a547","86f8f84a03a440feaf9ecc144dfdd797","7bbd8c8f7cde49a38a5c9e58a95d781f","93eaea46c1f642bd9503be5e6cc39605","8afad8f5dbe24a99aa639194afd08c96","5cfb1b5745b549ce8aa87300400e8a51","7df17420000c4f9c8fcfe3f3eb0cb087","4e2d4209a7b3434faefe669210e9f506","6d85ee70bff64ceb999801d7abce0f2e","44fcf17bca0a476cb65bbaa38b1373e7","6f0b4b12ee22435f90d1696ca9adb6ba","155d20676ebe4f5c95f1688734ce6266","0aabdb2db73a48178fba80e14c9c54c3","4481b0671d03482d9417d1833e63661b","374f5c4181ab4c69a301149f9f16fdcb","3c0ab6dc8df34b10acc476eae979de2d","aec1dfb6ed7f4286a0ee8a04113ca33d","ddefdeb98d2e4ac19e405277cd3182b0","7390cf47ba674639bb3acaf6a57c623b","f3a6439eedab42489da4a902cfc83ef6","9437120d512e4bae924598c884c4ac3e","c53bcb7a85084664b9e4e9ba20093c5b","c1f9330cc60e4fee9b815bd02f72556a","440ab32a1a154bb38254e4a9a498317f","c27cd7900c6846338bb6f16a3f98e9aa","b27f65abc89f41d3866400d2fcffdb6a","107db722b77a45fb8d9132a83ebefb14","1bf8d6676b4e40389b23122ccbe47889","1dfa3a2c3ccf4320b27402cfe92f8386","1beb630a63864d2b86faa1de9bd93b90","22d10173938d4475928b966603bd42e2","e1cb0ee5c73e4f4ca8b168632f70c2ff","82f0e6c48b694af39b006d88dc04c915","e49e24dd2fdb463996fe32c2e3ddf8be","85beef02278f4edd9c46b4f59adb3468","b903aa4157ce49009ebd9001159a9c45","def66c91f2474922a2c4b8b8f066642c","0463fc002c8b4d59b9c98cb92a8637c9","1ea43aec1600470784de0dd32fd0670c","09c3b9e916c342a5ba2c1469dc5e25bb","a03a8d6689754357ad875c3058290b17","bcd936cda8e74cce8f0abc5646f065a2","e14a3c4a763d40bf92596d3c046093e5","a704ab8188ac49d6a10f40245ac64ec4","ba7d101e35f04afaaf159bdd3c0b3a54","a879bd7dfbed4864a498b6781b9d36e1","587eca09df6b45d5bdab34170437cd93","eefba4d7059e465583338ccea29bdddd","3b31a9aa5e824553b40010227f23a373","7bea0e070a344060941dbec91e413a3b","c443caa8bf4b4039adbca70ad1667659","e3e5b6eb84ea48db812692b6976b1440","a232619d2dae4e15b88c6edee80a647c","acc97e089ece4eb0afabbf27c2d6b502","bf7b4811df0c43ee8916d2dc809f352a","08a67447d43e4b8c99ad8751012411a3","1cf0122f64744e108e348a65e788f480","03f14dd68ba7409891773e3076e922cc","53e458fbb7a346b4a7252b754af5b18e","e213a9c00d704cff930f430d74dbc702","3f72a723595c470da14dcf344766907a","646bc4126ed4403cab137159ba4e5c90","541bb7fb0fb646298f4066037ff78e66","ab4e5c9745ef4637b53f44cec1dc9d7d","e7cd250d95fa4b73a22e2cfca6a4e3d2","83024d08511c4bf79763d0d2e2fbd1a0","f1ec2f34ceb54e45a2216e1a167ee513"]},"outputId":"cd757c62-3180-45d2-ea82-fa8db5796150","executionInfo":{"status":"ok","timestamp":1660408371176,"user_tz":-120,"elapsed":1273035,"user":{"displayName":"","userId":""}}},"source":["defence_cleanse = cleanse(classifier, steps=10, learning_rate=0.1)\n","defence_cleanse.mitigate(clean_x_test, clean_y_test, mitigation_types=[\"filtering\"])"],"execution_count":19,"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0e20b9aa718d43dd8c6dd5635a3a4aaa","version_major":2,"version_minor":0},"text/plain":["Generating backdoor for class 0: 0%| | 0/10 [00:00