Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913919592824832 |
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| author | Roith, Tim Bungert, Leon Wacker, Philipp |
| author_facet | Roith, Tim Bungert, Leon Wacker, Philipp |
| contents | Consensus-based optimization (CBO) has established itself as an efficient gradient-free optimization scheme, with attractive mathematical properties, such as mean-field convergence results for non-convex loss functions. In this work, we study CBO in the context of closed-box adversarial attacks, which are imperceptible input perturbations that aim to fool a classifier, without accessing its gradient. Our contribution is to establish a connection between the so-called consensus hopping as introduced by Riedl et al. and natural evolution strategies (NES) commonly applied in the context of adversarial attacks and to rigorously relate both methods to gradient-based optimization schemes. Beyond that, we provide a comprehensive experimental study that shows that despite the conceptual similarities, CBO can outperform NES and other evolutionary strategies in certain scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_24048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Roith, Tim Bungert, Leon Wacker, Philipp Optimization and Control Machine Learning 65K10, 68Q32, 65K15, 90C26 Consensus-based optimization (CBO) has established itself as an efficient gradient-free optimization scheme, with attractive mathematical properties, such as mean-field convergence results for non-convex loss functions. In this work, we study CBO in the context of closed-box adversarial attacks, which are imperceptible input perturbations that aim to fool a classifier, without accessing its gradient. Our contribution is to establish a connection between the so-called consensus hopping as introduced by Riedl et al. and natural evolution strategies (NES) commonly applied in the context of adversarial attacks and to rigorously relate both methods to gradient-based optimization schemes. Beyond that, we provide a comprehensive experimental study that shows that despite the conceptual similarities, CBO can outperform NES and other evolutionary strategies in certain scenarios. |
| title | Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies |
| topic | Optimization and Control Machine Learning 65K10, 68Q32, 65K15, 90C26 |
| url | https://arxiv.org/abs/2506.24048 |