Shedding Light on some Leaks in PRNU-based Source Attribution

This is the official code implementation of the paper "Shedding Light on some Leaks in PRNU-based Source Attribution"

Authors: Andrea Montibeller, Roy Alia Asiku, Fernando Pérez-González, Giulia Boato

Contact: [email protected]

Abstract

Forensic image source attribution aims at deciding whether a query image was taken by a specific camera. While various algorithms leveraging forensic traces have been proposed, the most effective techniques rely on Photo Response Non-Uniformity (PRNU), a pattern introduced by camera sensors during the image acquisition process. In recent years, advances in image acquisition and processing technologies in modern devices have been found to impact the performance of PRNU, seemingly challenging its uniqueness. In this paper, we build upon recent discoveries of leaks in PRNU uniqueness, focusing on the dataset recently published by Iuliani et al. which has been instrumental in identifying numerous issues related to source attribution. Specifically, we analyze the effects in terms of false positive of visible watermarks applied to Xiaomi Mi 9 images, and reveal artifacts in the magnitude of the Discrete Fourier Transform of Samsung A50 images, indicative of the absence of non-unique artifacts. Furthermore, we demonstrate how several false positive cases are attributed to mislabeled devices. Finally, we show that a number of false negatives from the dataset are traceable to radially corrected images, and to images processed by third-party software that had not been previously noticed.

Requirements

Refer to this github repository for code requirements and to use the radial correction inversion method used in our paper.

List of Samsung A50 images source of False Positives

Here we report the list of images, used to compose the camera fingerprints of the Samsung A50, source of False Positives. If we use just these images to estimate the three Samsung A50 camera fingerprints, they will not expose any spikes in DFT domain, in constrast with the example below:

List of Post-Processed Images

Here we report complete list of images post-processed out-camera. Use "Software_outcamera_images.py" and modify line 35.