On the Adversarial Robustness of Out-of-distribution Generalization Models

Xin Zou · Weiwei Liu

Great Hall & Hall B1+B2 (level 1) #801
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Wed 13 Dec 8:45 a.m. PST — 10:45 a.m. PST


Out-of-distribution (OOD) generalization has attracted increasing research attention in recent years, due to its promising experimental results in real-world applications. Interestingly, we find that existing OOD generalization methods are vulnerable to adversarial attacks. This motivates us to study OOD adversarial robustness. We first present theoretical analyses of OOD adversarial robustness in two different complementary settings. Motivated by the theoretical results, we design two algorithms to improve the OOD adversarial robustness. Finally, we conduct experiments to validate the effectiveness of our proposed algorithms.

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