MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks
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 Bézier crossover operator that optimizes perturbations along a continuous Bézier 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 perturbat...
| Authors | Hyo Seo Kim, Gang Luo, Can Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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