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Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Francesco Croce · Sven Gowal · Thomas Brunner · Evan Shelhamer · Matthias Hein · Taylan Cemgil

Tue Jul 19 08:30 AM -- 08:35 AM (PDT) @ Hall G
in Deep Learning: Robustness »

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.

Keywords: DL: Robustness · SA: Trustworthy Machine Learning

Author Information

Francesco Croce (University of Tuebingen)
Sven Gowal (DeepMind)
Thomas Brunner (Technical University of Munich)
Evan Shelhamer (DeepMind)
Matthias Hein (University of Tübingen)
Taylan Cemgil (DeepMind)

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