Boosting Adversarial Training With Mitigating Hard Sample Interference

This research addresses a problem in adversarial training (a technique that teaches AI models to resist adversarial examples, which are inputs carefully designed to fool the model). When adversarial training tries to improve both normal accuracy and robustness at the same time, it struggles with hard samples (data points near the decision boundary where the model finds it difficult to classify correctly), often forcing a sacrifice of one goal for the other. The authors propose MHSI (mitigating hard sample interference), which uses two techniques: a weighted adaptive mechanism that helps the model focus more on learning clean samples, and a dynamic calibration strategy guided by gradient analysis that adjusts how the model handles hard samples, resulting in improved robustness without losing accuracy.