Adversarial attacks for mixtures of classifiers

• URL: http://arxiv.org/abs/2307.10788v1
• Date: Thu, 20 Jul 2023 11:38:55 GMT
• Title: Adversarial attacks for mixtures of classifiers
• Authors: Lucas Gnecco Heredia, Benjamin Negrevergne, Yann Chevaleyre
• Abstract summary: We discuss the problem of attacking a mixture in a principled way. We introduce two desirable properties of attacks based on a geometrical analysis of the problem. We then show that existing attacks do not meet both of these properties.
• Score: 7.612259653177203
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Mixtures of classifiers (a.k.a. randomized ensembles) have been proposed as a way to improve robustness against adversarial attacks. However, it has been shown that existing attacks are not well suited for this kind of classifiers. In this paper, we discuss the problem of attacking a mixture in a principled way and introduce two desirable properties of attacks based on a geometrical analysis of the problem (effectiveness and maximality). We then show that existing attacks do not meet both of these properties. Finally, we introduce a new attack called lattice climber attack with theoretical guarantees on the binary linear setting, and we demonstrate its performance by conducting experiments on synthetic and real datasets.

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