Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them

Florian Tramèr

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arXiv · 2107.11630

Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them

Florian Tramèr

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Abstract

Making classifiers robust to adversarial examples is hard. Thus, many defenses tackle the seemingly easier task of detecting perturbed inputs. We show a barrier towards this goal. We prove a general hardness reduction between detection and classification of adversarial examples: given a robust detector for attacks at distance {\epsilon} (in some metric), we can build a similarly robust (but inefficient) classifier for attacks at distance {\epsilon}/2. Our reduction is computationally inefficient, and thus cannot be used to build practical classifiers. Instead, it is a useful sanity check to test whether empirical detection results imply something much stronger than the authors presumably anticipated. To illustrate, we revisit 13 detector defenses. For 11/13 cases, we show that the claimed detection results would imply an inefficient classifier with robustness far beyond the state-of-the-art.

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Florian Tramèr. 2021-07-24. Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them. https://arxiv.org/abs/2107.11630

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