Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders
Hebi Li, Qi Xiao, Shixin Tian, Jin Wen Tian · arXiv (Cornell University) · 2019
Machine learning models are vulnerable to adversarial examples. Iterative adversarial training has shown promising results against strong white-box attacks. However, adversarial training is very expensive, and every time a model needs to be protected, such expensive training scheme needs to be performed. In this paper, we propose to apply iterative adversarial training scheme to an external auto-encoder, which once trained can be used to protect other models directly. We empirically show that our model outperforms other purifying-based methods against white-box attacks, and transfers well to directly protect other base models with different architectures.