Bridging Models to Defend: A Population-Based Strategy for Robust Adversarial Defense

• URL: http://arxiv.org/abs/2303.10225v2
• Date: Sun, 24 Aug 2025 14:37:09 GMT
• Title: Bridging Models to Defend: A Population-Based Strategy for Robust Adversarial Defense
• Authors: Ren Wang, Yuxuan Li, Can Chen, Dakuo Wang, Jinjun Xiong, Pin-Yu Chen, Sijia Liu, Mohammad Shahidehpour, Alfred Hero,
• Abstract summary: Adrial robustness is a critical measure of a neural network's ability to withstand adversarial attacks at inference time.<n>We propose a novel Robust Mode Connectivity (RMC)-oriented adversarial defense framework.<n>We show that our methods significantly improve robustness against $ell_infty$, $ell$, and hybrid attacks.
• Score: 70.39517230717126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial robustness is a critical measure of a neural network's ability to withstand adversarial attacks at inference time. While robust training techniques have improved defenses against individual $\ell_p$-norm attacks (e.g., $\ell_2$ or $\ell_\infty$), models remain vulnerable to diversified $\ell_p$ perturbations. To address this challenge, we propose a novel Robust Mode Connectivity (RMC)-oriented adversarial defense framework comprising two population-based learning phases. In Phase I, RMC searches the parameter space between two pre-trained models to construct a continuous path containing models with high robustness against multiple $\ell_p$ attacks. To improve efficiency, we introduce a Self-Robust Mode Connectivity (SRMC) module that accelerates endpoint generation in RMC. Building on RMC, Phase II presents RMC-based optimization, where RMC modules are composed to further enhance diversified robustness. To increase Phase II efficiency, we propose Efficient Robust Mode Connectivity (ERMC), which leverages $\ell_1$- and $\ell_\infty$-adversarially trained models to achieve robustness across a broad range of $p$-norms. An ensemble strategy is employed to further boost ERMC's performance. Extensive experiments across diverse datasets and architectures demonstrate that our methods significantly improve robustness against $\ell_\infty$, $\ell_2$, $\ell_1$, and hybrid attacks. Code is available at https://github.com/wangren09/MCGR.

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