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arXiv · 2506.24048

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies

Tim Roith·Leon Bungert·Philipp Wacker
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Abstract

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.

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Tim Roith, Leon Bungert, Philipp Wacker. 2025-06-30. Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies. https://arxiv.org/abs/2506.24048

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