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arXiv · 1905.10729

Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders

Hebi Li·Qi Xiao·Shixin Tian·Jin Tian
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Abstract

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.

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Hebi Li, Qi Xiao, Shixin Tian, Jin Tian. 2019-05-26. Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders. https://arxiv.org/abs/1905.10729

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