Adaptive defenses, which optimize at test time, promise to improve
adversarial robustness. We categorize such adaptive test-time defenses, explain
their potential benefits and drawbacks, and evaluate a representative variety
of the latest adaptive defenses for image classification. Unfortunately, none
significantly improve upon static defenses when subjected to our careful case
study evaluation. Some even weaken the underlying static model while
simultaneously increasing inference computation. 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 for their
thorough evaluation. We extend the checklist of Carlini et al. (2019) by
providing concrete steps specific to adaptive defenses.

我们对自适应防御策略进行了分类并评估了最新的代表性方法，结果表明在图像分类中这些自适应防御策略没有明显的提高静态防御的水平，同时增加了推理计算。然而，我们仍相信自适应的防御策略是一个有前途的研究方向，并提出了具体的研究建议和评估步骤扩展了 Carlini 等人的清单。