Towards everywhere reliable classification - A joint framework for adversarial robustness and out-of-distribution detection

到目前为止，攻击健壮性和分布外(OOD)检测一直被分开处理。然而，在我们看来，这些问题的分离是人为的，因为它们内在地相互联系。只有超越Madry等人提出的经典对抗训练框架，才有可能在超越训练时使用的威胁模型的基础上，在对抗健壮性方面取得进展。并将面向对象的检测和对手攻击的健壮性合并到一个框架中。

Adversarial robustness and out-of-distribution (OOD) detection have been treated separately so far. However, the separation of these problems is in our point of view artificial as they are inherently linked to each other. Advances in in adversarial robustness generalizing beyond the threat models used at training time seem possible only by going beyond the classical adversarial training framework proposed by Madry et al. and merging OOD detection and adversarial robustness in a single framework.