Adversarial attacks for mixtures of classifiers
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 20 Jul 2023.
Builds on
21 of 33 references.
References · 21
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9 more
- 2021TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothnesscited by 38
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