AI 中文总结
针对长尾对抗训练的双重失衡问题，提出即插即用框架CGRm，通过定向鲁棒误差等技术提升脆弱类鲁棒性，在长尾基准上实现鲁棒性能提升。
AI 中文摘要
长尾分布下的对抗训练面临双重失衡：类别不平衡使训练目标偏向头部类，而对抗内部最大化可能进一步放大这种偏差。现有方法通过校正类别先验或调整类别级鲁棒监督来缓解此问题，但它们孤立处理每个类别，无法识别导致长尾崩溃的边界。我们提出用于长尾对抗训练的混淆几何重平衡方法（Confusion Geometry Rebalancing method，CGRm），这是一种即插即用框架，利用定向鲁棒误差作为训练信号。CGRm利用周期性鲁棒评估推导源类别损失权重、类别级鲁棒系数和定向混淆几何图，随后将反馈加权鲁棒优化与图引导间隔校正相结合，从而提升脆弱类的鲁棒性并锐化导致长尾性能下降的关键边界。在长尾基准上的实验表明，CGRm相比现有方法取得了一致的鲁棒性能提升， ablation研究验证了每个组件的贡献，我们在补充材料中提供了代码。
英文摘要
Adversarial training under long tailed distributions suffers from a dual imbalance: the class imbalance skews the training objective toward head classes, and the adversarial inner maximization may further amplify this bias. Existing methods mitigate this issue by correcting class priors or adapting class wise robust supervision, yet they treat each class in isolation and fail to identify which boundaries drive long tailed collapse. We propose a Confusion Geometry Rebalancing method (CGRm) for long tail adversarial training, a plug in framework that leverages directed robust errors as training signals. CGRm leverages periodic robust evaluations to derive source class loss weights, class wise robust coefficients, and a directed confusion geometry graph. The method then couples feedback weighted robust optimization with graph guided margin correction, thereby boosting the robustness of vulnerable classes and sharpening the critical boundaries that drive long tailed performance degradation. Experiments on long tailed benchmarks show that CGRm achieves consistent robust performance gains over existing methods, with ablations validating the contribution of each component. We provide the code in the supplement.
