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

2026-05-18

AI Summary

This work addresses the limitations of traditional evolutionary attacks, where discrete crossover operations often disrupt the structural integrity of adversarial perturbations, leading to inefficient search and poor transferability. To overcome this, the authors propose MoCo-EA, a novel method that introduces Bézier curves into evolutionary attacks for the first time, constructing continuous paths between parent perturbations and optimizing intermediate points along these trajectories. This approach reveals a pattern connectivity property of adversarial examples: intermediate perturbations exhibit stronger transferability than endpoint ones. By integrating a Bézier-based continuous crossover operator with gradient-free evolutionary search, MoCo-EA substantially reduces query counts and convergence time while simultaneously improving attack success rates and cross-model transferability.

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 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 perturbations exhibit mode connectivity; (2) Intermediate points along optimized paths achieve higher transferability than endpoints; (3) Bézier 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.

Problem

• evolutionary attacks
• adversarial perturbations
• crossover inefficiency
• mode connectivity
• gradient-free optimization

Innovation

• Mode Connectivity
• Bézier Crossover
• Evolutionary Adversarial Attack
• Transferability
• Query Efficiency