Adversarial Purification with the Manifold Hypothesis

In this work, we formulate a novel framework for adversarial robustness using the manifold hypothesis. This framework provides sufficient conditions for defending against adversarial examples. We develop an adversarial purification method with this framework. Our method combines manifold learning with variational inference to provide adversarial robustness without the need for expensive adversarial training. Experimentally, our approach can provide adversarial robustness even if attackers are aware of the existence of the defense. In addition, our method can also serve as a test-time defense mechanism for variational autoencoders.

Authors

• Zhaoyuan Yang GE Research
• Zhiwei Xu School of Artificial Intelligence, Shandong University
• Jing Zhang
• Richard Hartley Australian National University
• Peter Tu GE Research

Keywords

• ML: Adversarial Learning & Robustness
• ML: Deep Generative Models & Autoencoders