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

Xin Zou; Weiwei Liu

NeurIPS 2023poster13 citations

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

Abstract

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.

Adversarial RobustnessOut-of-distribution Generalization

BibTeX

@inproceedings{
zou2023on,
title={On the Adversarial Robustness of Out-of-distribution Generalization Models},
author={Xin Zou and Weiwei Liu},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=IiwTFcGGTq}
}