Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers

Lucas Gnecco-Heredia; Benjamin Negrevergne; Yann Chevaleyre

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arXiv · 2506.10888

Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers

Lucas Gnecco-Heredia·Benjamin Negrevergne·Yann Chevaleyre

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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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Lucas Gnecco-Heredia, Benjamin Negrevergne, Yann Chevaleyre. 2025-06-12. Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers. https://arxiv.org/abs/2506.10888

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