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Can Adversarial Training Be Manipulated By Non-Robust Features?
Lue Tao · Lei Feng · Hongxin Wei · Jinfeng Yi · Sheng-Jun Huang · Songcan Chen

Tue Dec 06 05:00 PM -- 07:00 PM (PST) @
in Lightning Talks 2A-2 »
Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. This defense ability, however, is challenged in this paper. We identify a novel threat model named stability attack, which aims to hinder robust availability by slightly manipulating the training data. Under this threat, we show that adversarial training using a conventional defense budget $\epsilon$ provably fails to provide test robustness in a simple statistical setting, where the non-robust features of the training data can be reinforced by $\epsilon$-bounded perturbation. Further, we analyze the necessity of enlarging the defense budget to counter stability attacks. Finally, comprehensive experiments demonstrate that stability attacks are harmful on benchmark datasets, and thus the adaptive defense is necessary to maintain robustness.
Keywords: adversarial training · Adversarial Robustness · Availability Attacks · Hypocritical Perturbations
[ Slides] 

Author Information

Lue Tao (Nanjing University)
Lei Feng (Nanyang Technological University, Singapore)
Hongxin Wei (Nanyang Technological University)
Jinfeng Yi (JD AI Research)
Sheng-Jun Huang (Nanjing University of Aeronautics and Astronautics)
Songcan Chen (Nanjing University of Aeronautics and Astronautics)

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