Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

Adaptive defenses that use test-time optimization promise to improve robustness to adversarial examples. We categorize such adaptive test-time defenses and explain their potential benefits and drawbacks. In the process, we evaluate some of the latest proposed adaptive defenses (most of them published at peer-reviewed conferences). Unfortunately, none significantly improve upon static models when evaluated appropriately. Some even weaken the underlying static model while simultaneously increasing inference cost. While these results are disappointing, we still believe that adaptive test-time defenses are a promising avenue of research and, as such, we provide recommendations on evaluating such defenses. We go beyond the checklist provided by Carlini et al. (2019) by providing concrete steps that are specific to this type of defense.