defense arXiv Aug 25, 2025 · Aug 2025
Quanwei Wu, Jun Guo, Wei Wang et al. · Dongguan University of Technology · The Hong Kong University of Science and Technology
Proposes feature-space adapter for adversarial training that eliminates robust overfitting with negligible computational overhead
Input Manipulation Attack vision
Adversarial training (AT) with projected gradient descent is the most popular method to improve model robustness under adversarial attacks. However, computational overheads become prohibitively large when AT is applied to large backbone models. AT is also known to have the issue of robust overfitting. This paper contributes to solving both problems simultaneously towards building more trustworthy foundation models. In particular, we propose a new adapter-based approach for efficient AT directly in the feature space. We show that the proposed adapter-based approach can improve the inner-loop convergence quality by eliminating robust overfitting. As a result, it significantly increases computational efficiency and improves model accuracy by generalizing adversarial robustness to unseen attacks. We demonstrate the effectiveness of the new adapter-based approach in different backbone architectures and in AT at scale.
cnn transformer Dongguan University of Technology · The Hong Kong University of Science and Technology
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