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Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
Lue Tao · Lei Feng · Jinfeng Yi · Sheng-Jun Huang · Songcan Chen

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @ Virtual #None
Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples. By formalizing this malicious attack as finding the worst-case training data within a specific $\infty$-Wasserstein ball, we show that minimizing adversarial risk on the perturbed data is equivalent to optimizing an upper bound of natural risk on the original data. This implies that adversarial training can serve as a principled defense against delusive attacks. Thus, the test accuracy decreased by delusive attacks can be largely recovered by adversarial training. To further understand the internal mechanism of the defense, we disclose that adversarial training can resist the delusive perturbations by preventing the learner from overly relying on non-robust features in a natural setting. Finally, we complement our theoretical findings with a set of experiments on popular benchmark datasets, which show that the defense withstands six different practical attacks. Both theoretical and empirical results vote for adversarial training when confronted with delusive adversaries.

Author Information

Lue Tao (Nanjing University of Aeronautics and Astronautics)
Lei Feng (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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