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arXiv · 2605.18919

MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks

Hyo Seo Kim·Gang Luo·Can Chen·Binghui Wang·Yue Duan·Ren Wang
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Abstract

Evolutionary algorithms for adversarial attacks leverage population-based search to discover perturbations without gradient information, but suffer from inefficient crossover operations that destroy adversarial properties through discrete interpolation. We introduce Mode Connectivity Evolutionary Attack (MoCo-EA), which replaces traditional crossover with a novel B\'ezier crossover operator that optimizes perturbations along a continuous B\'ezier curve between parent perturbations. Our key insight is that adversarial examples lie on connected manifolds where intermediate points maintain and often enhance attack effectiveness. We demonstrate three findings: (1) Successful adversarial perturbations exhibit mode connectivity; (2) Intermediate points along optimized paths achieve higher transferability than endpoints; (3) B\'ezier crossover dramatically outperforms discrete genetic operations while reducing convergence time and query requirements. By exploiting the geometric structure of adversarial space through path optimization, MoCo-EA provides an efficient and reliable method. Our work challenges the traditional view of adversarial examples as isolated points and opens new directions for both attack generation and defense research.

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Hyo Seo Kim, Gang Luo, Can Chen, Binghui Wang, Yue Duan, Ren Wang. 2026-05-18. MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks. https://arxiv.org/abs/2605.18919

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