Timezone: »

 
Spotlight
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Kevin Roth · Yannic Kilcher · Thomas Hofmann

Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
in Orals & Spotlights Track 20: Social/Adversarial Learning »
We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks. Specifically, we prove that $l_p$-norm constrained projected gradient ascent based adversarial training with an $l_q$-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.

Author Information

Kevin Roth (ETH Zurich)
Yannic Kilcher (ETH Zurich)
Thomas Hofmann (ETH Zurich)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors