Zero Knowledge Adversarial Defense Via Iterative Translation Cycle

Image classification networks based on deep learning are found to be vulnerable to the carefully designed adversarial examples. The existing approaches are still far from solving this problem. In this paper, we propose iterative translation cycle GAN (ITC-GAN) to jointly optimize generators and discriminators, so as to defense against adversarial examples with zero knowledge of adversarial attacks. We train two generators to form an iterative translation cycle which changes deep features of the input images and then reconstructs them. As the cycle of image translation can be conducted iteratively, the noises of adversarial examples are gradually eliminated. We only use clean images to train the whole ITC-GAN, so our method is not coupled to specific attack methods and specific classifiers. We conduct experiments on MNIST, CIFAR10, and ImageNet50. The experimental results demonstrate that ITC-GAN is more robust and flexible than state-of-the-art methods in different adversarial settings.