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Thomas Brunner

2 accepted papers

2022

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

ICML 2022spotlight

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, no…

2019

Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks

ICCV 2019poster

We consider adversarial examples for image classification in the black-box decision-based setting. Here, an attacker cannot access confidence scores, but only the final label. Most attacks for this scenario are either unreliable or inefficient. Focusing on the latter, we show that a specific class o…

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