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

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.

Optimization and Control (math.OC); Machine Learning (cs.LG)

arXiv:2506.24048 [math.OC]

(or arXiv:2506.24048v2 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2506.24048 Focus to learn more arXiv-issued DOI via DataCite

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