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

• URL: http://arxiv.org/abs/2506.24048v1
• Date: Mon, 30 Jun 2025 16:54:44 GMT
• Title: Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
• Abstract summary: Consensus-based optimization (CBO) has established itself as an efficient gradient-free optimization scheme.<n>Our study is as introduced by Riedl et al. and natural evolution strategies (NES) commonly applied in adversarial attacks.<n>Despite the conceptual similarities, CBO can outperform NES and other evolutionary strategies in certain scenarios.
• Score: 4.813333335683418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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