Improving Adversarial Robustness by Mitigating Instability through Relearning

Adversarial training is the most effective defense against adversarial attacks, but it often suffers from robust overfitting, whose causes remain unclear. In this paper, we observe a counterintuitive phenomenon: the model robust stability consistently deteriorates during training. To quantify this behavior, we introduce the Robust Stability Rate (RSR) and define robust instability. Our analysis demonstrates that improving the Robust Stability Rate can effectively reduce robust instability, thereby mitigating robust overfitting. Building on this insight, we propose a novel framework called Relearning Adversarial Training (RAT). Our min-max optimization establishes a new adversarial dynamic where generated adversarial examples induce robust instability, while model training enforces robust stability, aiming to enhance the model Robust Stability Rate of the current learning model by leveraging knowledge from historical model. Experimental results demonstrate that RAT can be integrated with various methods to significantly enhance their performance. It effectively reduces robust instability and mitigates robust overfitting.