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CoMGS: A Multi-Objective Coevolutionary Algorithm for Transferable Adversarial Attacks
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DOI：10.1109/tevc.2026.3725853.png)
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Abstract
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Transfer-based adversarial attacks exploit surrogate models to generate perturbations that can fool unseen target models without any target queries. Existing methods formulate this attack as a constrained single-objective optimization problem. However, in the non-convex adversarial loss landscape, conventional single-objective optimizers are susceptible to local optima and gradient masking, making it difficult to balance visual imperceptibility and cross-model attack transferability. In this paper, we propose a multi-objective coevolutionary algorithm with multi-scale gradient smoothing (CoMGS). Specifically, the proposed CoMGS formulates transferable adversarial example generation as a bi-objective optimization problem and uses two coevolutionary sub-populations, namely the attack population and the shrinking population. Within this coevolutionary framework, the attack population integrates heterogeneous multi-scale gradient estimation operators with variable-radius neighborhood smoothing to alleviate gradient masking and reduce the risk of convergence to deceptive local optima. To complement this exploration-oriented search, the shrinking population adopts an analytical gradient descent operator to deterministically shrink perturbations, thereby improving population diversity. Experimental results demonstrate that CoMGS mitigates the dimensional bottleneck of conventional multi-objective evolutionary algorithms and achieves higher black-box attack success rate (ASR) than the baseline methods in most evaluated settings. On the Caltech-UCSD Birds dataset with ResNeXt50_32x4d as the surrogate model, CoMGS improves the overall average ASR by 9.1% over the strongest single-objective baseline.
Keywords:
Adversarial attacks
adversarial transferability
multi-objective optimization
coevolutionary algorithm
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