diff --git a/code_soup/common/perturbation.py b/code_soup/common/perturbation.py
new file mode 100644
index 0000000..b2e023d
--- /dev/null
+++ b/code_soup/common/perturbation.py
@@ -0,0 +1,12 @@
+from abc import ABC, abstractmethod
+
+
+class Perturbation(ABC):
+    """
+    Docstring for Abstract Class Perturbation
+    """
+
+    @classmethod
+    @abstractmethod
+    def __init__(self):
+        pass
diff --git a/code_soup/common/vision/perturbations.py b/code_soup/common/vision/perturbations.py
new file mode 100644
index 0000000..bdc85cf
--- /dev/null
+++ b/code_soup/common/vision/perturbations.py
@@ -0,0 +1,70 @@
+from abc import abstractmethod
+from typing import Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from math import log10
+
+from code_soup.common.perturbation import Perturbation
+
+
+class VisualPerturbation(Perturbation):
+    """
+    An abstract method for various Visual Perturbation Metrics
+        Methods
+        __init__(self, original : Union[np.ndarray, torch.Tensor], perturbed: Union[np.ndarray, torch.Tensor])
+            - init method
+    """
+
+    def __init__(
+        self,
+        original: Union[np.ndarray, torch.Tensor],
+        perturbed: Union[np.ndarray, torch.Tensor],
+    ):
+        """
+        Docstring
+        #Automatically cast to Tensor using the torch.from_numpy() in the __init__ using if
+        """
+
+        if type(original) == torch.Tensor:
+            self.original = original
+        else:
+            self.original = torch.from_numpy(original)
+        print(self.original.shape)
+
+        if type(perturbed) == torch.Tensor:
+            self.perturbed = perturbed
+        else:
+            self.perturbed = torch.from_numpy(perturbed)
+
+    def flatten(self, array : torch.tensor) -> torch.Tensor:
+        return array.flatten()
+
+    def totensor(self, array : np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(array)
+
+    def subtract(self,original : torch.Tensor, perturbed : torch.Tensor) -> torch.Tensor:
+        return torch.sub(original, perturbed)
+
+    def calculate_LPNorm(self, p: Union[int, str]) -> float:
+        if p == 'inf':
+            return torch.linalg.vector_norm(self.flatten(self.subtract(self.original,self.perturbed)), ord = float('inf')).item()
+        elif p == 'fro':
+            return self.calculate_LPNorm(2)
+        else:
+            return torch.linalg.norm(self.flatten(self.subtract(self.original,self.perturbed)), ord = p).item()
+
+    def calculate_PSNR(self) -> float:
+        return 20 * log10(1.0/self.calculate_RMSE())
+
+    def calculate_RMSE(self) -> float:
+        loss = nn.MSELoss()
+        return (loss(self.original, self.perturbed)**0.5).item()
+
+    def calculate_SAM(self):
+        raise NotImplementedError
+
+    def calculate_SRE(self):
+        raise NotImplementedError
diff --git a/tests/test_common/test_vision/test_perturbations.py b/tests/test_common/test_vision/test_perturbations.py
new file mode 100644
index 0000000..eb71992
--- /dev/null
+++ b/tests/test_common/test_vision/test_perturbations.py
@@ -0,0 +1,60 @@
+import random
+import unittest
+
+import numpy as np
+import torch
+from torchvision.datasets.fakedata import FakeData
+from torchvision.transforms import ToTensor
+
+from code_soup.common.vision.perturbations import VisualPerturbation
+
+
+class TestVisualPerturbation(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        torch.manual_seed(42)
+        np.random.seed(42)
+        random.seed(42)
+        df = FakeData(size=2, image_size=(3, 64, 64))
+        a, b = tuple(df)
+        a, b = ToTensor()(a[0]).unsqueeze_(0), ToTensor()(b[0]).unsqueeze_(0)
+        cls.obj_tensor = VisualPerturbation(original=a, perturbed=b)
+        cls.obj_numpy = VisualPerturbation(original=a.numpy(), perturbed=b.numpy())
+
+    def test_LPNorm(self):
+        self.assertAlmostEqual(
+            TestVisualPerturbation.obj_tensor.calculate_LPNorm(p=1), 4143.0249, places=3
+        )
+        self.assertAlmostEqual(
+            TestVisualPerturbation.obj_numpy.calculate_LPNorm(p="fro"),
+            45.6525,
+            places=3,
+        )
+
+    def test_PSNR(self):
+        self.assertAlmostEqual(
+            TestVisualPerturbation.obj_tensor.calculate_PSNR(),
+            33.773994480876496,
+            places=3,
+        )
+
+    def test_RMSE(self):
+        self.assertAlmostEqual(
+            TestVisualPerturbation.obj_tensor.calculate_RMSE(),
+            0.018409499898552895,
+            places=3,
+        )
+
+    def test_SAM(self):
+        self.assertAlmostEqual(
+            TestVisualPerturbation.obj_tensor.calculate_SAM(),
+            89.34839413786915,
+            places=3,
+        )
+
+    def test_SRE(self):
+        self.assertAlmostEqual(
+            TestVisualPerturbation.obj_tensor.calculate_SRE(),
+            41.36633261587073,
+            places=3,
+        )