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Poster
Increasing Confidence in Adversarial Robustness Evaluations
Roland S. Zimmermann · Wieland Brendel · Florian Tramer · Nicholas Carlini

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #512
in Poster Session 4 »

Hundreds of defenses have been proposed to make deep neural networks robust against minimal (adversarial) input perturbations. However, only a handful of these defenses held up their claims because correctly evaluating robustness is extremely challenging: Weak attacks often fail to find adversarial examples even if they unknowingly exist, thereby making a vulnerable network look robust. In this paper, we propose a test to identify weak attacks and, thus, weak defense evaluations. Our test slightly modifies a neural network to guarantee the existence of an adversarial example for every sample. Consequentially, any correct attack must succeed in breaking this modified network. For eleven out of thirteen previously-published defenses, the original evaluation of the defense fails our test, while stronger attacks that break these defenses pass it. We hope that attack unit tests - such as ours - will be a major component in future robustness evaluations and increase confidence in an empirical field that is currently riddled with skepticism.

Keywords: Adversarial Robustness · robustness · adversarial attack
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Roland S. Zimmermann (University of Tübingen, International Max Planck Research School for Intelligent Systems)
Wieland Brendel (AG Bethge, University of Tübingen)
Florian Tramer (ETH Zurich)
Nicholas Carlini (Google)

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