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arXiv · 2506.15685

Ignition Phase : Standard Training for Fast Adversarial Robustness

Wang Yu-Hang·Liu ying·Fang liang·Wang Xuelin·Junkang Guo·Shiwei Li·Lei Gao·Jian Liu·Wenfei Yin
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Abstract

Adversarial Training (AT) is a cornerstone defense, but many variants overlook foundational feature representations by primarily focusing on stronger attack generation. We introduce Adversarial Evolution Training (AET), a simple yet powerful framework that strategically prepends an Empirical Risk Minimization (ERM) phase to conventional AT. We hypothesize this initial ERM phase cultivates a favorable feature manifold, enabling more efficient and effective robustness acquisition. Empirically, AET achieves comparable or superior robustness more rapidly, improves clean accuracy, and cuts training costs by 8-25\%. Its effectiveness is shown across multiple datasets, architectures, and when augmenting established AT methods. Our findings underscore the impact of feature pre-conditioning via standard training for developing more efficient, principled robust defenses. Code is available in the supplementary material.

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Wang Yu-Hang, Liu ying, Fang liang, Wang Xuelin, Junkang Guo, Shiwei Li, Lei Gao, Jian Liu, Wenfei Yin. 2025-10-11. Ignition Phase : Standard Training for Fast Adversarial Robustness. https://arxiv.org/abs/2506.15685

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