Incremental learning of GAN for detecting multiple adversarial attacks

Abstract

Neural networks are vulnerable to adversarial attack. Carefully crafted small perturbations can cause misclassification of neural network classifiers. As adversarial attack is a serious potential problem in many neural network based applications and new attacks always come up, it’s urgent to explore the detection strategies that can adapt new attacks quickly. Moreover, the detector is hard to train with limited samples. To solve these problems, we propose a GAN based incremental learning framework with Jacobian-based data augmentation to detect adversarial samples. To prove the proposed framework works on multiple adversarial attacks, we implement FGSM, LocSearchAdv, PSO-based attack on MNIST and CIFAR-10 dataset. The experiments show that our detection framework performs well on these adversarial attacks.