asranand7 / Adverserial_Attack

Different Adversarial attack methods implemented in PyTorch on CIFAR-10 Dataset

This repository contains Adversarial Attacks on CIFAR-10 dataset implemented in Pytorch:

1. Fast Gradient Sign Method (Untargeted)
2. Iterative Fast Gradient Sign Method (Untargeted)
3. DeepFool

It will include more Adversarial Attacks and Defenses Technique in future as well

*) The CIFAR-10 Network is trained on VGG-16 architecture based on [1] . It reaches a Test accuracy of 88%

Results : ( Test Accuracy is evaluated after adding adverserial noise to the test data)

    1) FGSM method:
          a) Epsilon = 0.1  Test Accuracy = 48.26 %
          b) Epsilon = 0.15 Test Accuracy = 43.26 %
          c) Epsilon = 0.2  Test Accuracy = 40.05 %

    2) Iterative FGSM Method:
          a) Epsilon = 0.075
                i) Iterations = 4   Test Accuracy = 33.06 %
                ii) Iterations = 10  Test Accuracy = 28.27 %
          b) Epsilon = 0.1
                i) Iterations = 4    Test Accuracy = 23.52 %
                ii) Iterations = 10   Test Accuracy = 17.98 %
          c) Epsilon = 0.15
                i) Iterations = 4     Test Accuracy = 12.7%
                ii) Iterations = 10   Test Accuract = 6.77 %
                iii) Iterations = 15  Test Accuracy = 5.93 %

References:

[1] Shuying Liu and Weihong Deng. Very deep convolutional neural network based image classifi- cation using small training sample size. In Pattern Recognition (ACPR), 2015 3rd IAPR Asian Conference on, pages 730–734. IEEE, 2015.

[2] Goodfellow, Ian J, Shlens, Jonathon and Szegedy, Christian. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 (2014): .

[3] S. Moosavi-Dezfooli, A. Fawzi and P. Frossard, "DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 2574-2582.