CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification

Abstract

Adversarial attack is aimed at fooling a target classifier with imperceptible perturbation. Adversarial examples, which are carefully crafted with a malicious purpose, can lead to erroneous predictions, resulting in catastrophic accidents. To mitigate the effect of adversarial attacks, we propose a novel purification model called CAP-GAN. CAP-GAN considers the idea of pixel-level and feature-level consistency to achieve reasonable purification under cycle-consistent learning. Specifically, we utilize a guided attention module and knowledge distillation to convey meaningful information to the purification model. Once the model is fully trained, inputs are projected into the purification model and transformed into clean-like images. We vary the capacity of the adversary to argue the robustness against various types of attack strategies. On CIFAR-10 dataset, CAP-GAN outperforms other pre-processing based defenses under both black-box and white-box settings.