Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers

• URL: http://arxiv.org/abs/2506.10888v1
• Date: Thu, 12 Jun 2025 16:53:32 GMT
• Title: Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers
• Authors: Lucas Gnecco-Heredia, Benjamin Negrevergne, Yann Chevaleyre,
• Abstract summary: We introduce two desirable properties of attacks based on a geometrical analysis of a problem (effectiveness and maximality)<n>We then show that existing attacks do not meet both of these properties.<n>We introduce a new attack called em lattice climber attack with theoretical guarantees in the binary linear setting, and demonstrate its performance by conducting experiments on synthetic and real datasets.
• Score: 5.38274042816001
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Finite mixtures of classifiers (a.k.a. randomized ensembles) have been proposed as a way to improve robustness against adversarial attacks. However, existing attacks have been shown to not suit this kind of classifier. 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 {\em lattice climber attack} with theoretical guarantees in the binary linear setting, and demonstrate its performance by conducting experiments on synthetic and real datasets.

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