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arXiv · 1906.01527

Adversarial Training is a Form of Data-dependent Operator Norm Regularization

Kevin Roth·Yannic Kilcher·Thomas Hofmann
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Abstract

We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks. Specifically, we prove that $\ell_p$-norm constrained projected gradient ascent based adversarial training with an $\ell_q$-norm loss on the logits of clean and perturbed inputs is equivalent to data-dependent (p, q) operator norm regularization. This fundamental connection confirms the long-standing argument that a network's sensitivity to adversarial examples is tied to its spectral properties and hints at novel ways to robustify and defend against adversarial attacks. We provide extensive empirical evidence on state-of-the-art network architectures to support our theoretical results.

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Kevin Roth, Yannic Kilcher, Thomas Hofmann. 2020-10-23. Adversarial Training is a Form of Data-dependent Operator Norm Regularization. https://arxiv.org/abs/1906.01527

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