# On Norm-Agnostic Robustness of Adversarial Training

Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

## Abstract

Adversarial examples are carefully perturbed in-puts for fooling machine learning models. A well-acknowledged defense method against such examples is adversarial training, where adversarial examples are injected into training data to increase robustness. In this paper, we propose a new attack to unveil an undesired property of the state-of-the-art adversarial training, that is it fails to obtain robustness against perturbations in $\ell_2$ and $\ell_\infty$ norms simultaneously. We discuss a possible solution to this issue and its limitations as well.
Original language Undefined/Unknown Arxiv preprint Published - May 15 2019 Yes

• cs.LG
• cs.CR
• stat.ML