Taming the Long Tail: Rebalancing Adversarial Training via Adaptive Perturbation

Deep neural networks are highly vulnerable to adversarial examples, i.e.,small perturbations that can significantly degrade model performance. While adversarial training has become the primary defense strategy, most studies focus on balanced datasets, overlooking the challenges posed by real-world long-tail data. Motivated by the fact that perturbations in adversarial examples inherently alter the training distribution, we theoretically investigate their impact. We first revisit adversarial training for long-tail data and identify two key limitations: (i) a skewed training objective caused by class imbalance, and (ii) unstable evolution of adversarial distributions. Furthermore, we show that perturbations can simultaneously address both adversarial vulnerability and class imbalance. Based on these insights, we propose Rebalanced Adversarial Intensity for Long-Tailed Data (RAIL), a plug-and-play framework that adaptively adjusts perturbations during adversarial training. Extensive experiments demonstrate that RAIL consistently enhances adversarial robustness and class-balance on long-tailed datasets.