Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911002274037760 |
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| author | Gnecco-Heredia, Lucas Negrevergne, Benjamin Chevaleyre, Yann |
| author_facet | Gnecco-Heredia, Lucas Negrevergne, Benjamin Chevaleyre, Yann |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers Gnecco-Heredia, Lucas Negrevergne, Benjamin Chevaleyre, Yann Machine Learning 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. |
| title | Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.10888 |