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This paper investigates catastrophic overfitting (CO) and the robustness-accuracy trade-off in Fast Adversarial Training (FAT), identifying low-confidence samples as key drivers of these issues. To address this, they propose Distribution-aware Dynamic Guidance (DDG), which dynamically adjusts perturbation budgets and supervision signals based on sample confidence and prediction state. DDG, combined with a weighted regularization constraint, significantly alleviates CO and improves the robustness-accuracy trade-off on standard benchmarks.
Low-confidence training samples are secretly sabotaging your fast adversarial training, leading to catastrophic overfitting and a worse robustness-accuracy trade-off.
Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representations. However, FAT often suffers from catastrophic overfitting (CO), where the model overfits to the training attack and fails to generalize to unseen ones. Moreover, robustness oriented optimization typically leads to notable performance degradation on clean inputs, and such degradation becomes increasingly severe as the perturbation budget grows. In this work, we conduct a comprehensive analysis of how guidance strength affects model performance by modulating perturbation and supervision levels across distinct confidence groups. The findings reveal that low confidence samples are the primary contributors to CO and the robustness accuracy trade off. Building on this insight, we propose a Distribution-aware Dynamic Guidance (DDG) strategy that dynamically adjusts both the perturbation budget and supervision signal. Specifically, DDG scales the perturbation magnitude according to the sample confidence at the ground truth class, thereby guiding samples toward consistent decision boundaries while mitigating the influence of learning spurious correlations. Simultaneously, it dynamically adjusts the supervision signal based on the prediction state of each sample, preventing overemphasis on incorrect signals. To alleviate potential gradient instability arising from dynamic guidance, we further design a weighted regularization constraint. Extensive experiments on standard benchmarks demonstrate that DDG effectively alleviates both CO and the robustness accuracy trade off.
