Related papers: Certified Distributional Robustness on Smoothed Classifiers

Certified Distributional Robustness on Smoothed Classifiers

URL: http://arxiv.org/abs/2010.10987v2
Date: Fri, 30 Apr 2021 07:24:47 GMT
Title: Certified Distributional Robustness on Smoothed Classifiers
Authors: Jungang Yang, Liyao Xiang, Ruidong Chen, Yukun Wang, Wei Wang, Xinbing Wang
Abstract summary: We propose the worst-case adversarial loss over input distributions as a robustness certificate. By exploiting duality and the smoothness property, we provide an easy-to-compute upper bound as a surrogate for the certificate.
Score: 27.006844966157317
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The robustness of deep neural networks (DNNs) against adversarial example attacks has raised wide attention. For smoothed classifiers, we propose the worst-case adversarial loss over input distributions as a robustness certificate. Compared with previous certificates, our certificate better describes the empirical performance of the smoothed classifiers. By exploiting duality and the smoothness property, we provide an easy-to-compute upper bound as a surrogate for the certificate. We adopt a noisy adversarial learning procedure to minimize the surrogate loss to improve model robustness. We show that our training method provides a theoretically tighter bound over the distributional robust base classifiers. Experiments on a variety of datasets further demonstrate superior robustness performance of our method over the state-of-the-art certified or heuristic methods.

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