Keywords: adversarial examples, robust machine learning, robust optimization, deep feature representations
TL;DR: We show that adversarial robustness might come at the cost of standard classification performance, but also yields unexpected benefits.
Abstract: We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the features learned by robust models tend to align better with salient data characteristics and human perception.
Code: [![Papers with Code](/images/pwc_icon.svg) 7 community implementations](https://paperswithcode.com/paper/?openreview=SyxAb30cY7)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 9 code implementations](https://www.catalyzex.com/paper/arxiv:1805.12152/code)