A3GT: An Adaptive Asynchronous Generalized Adversarial Training Method

Abstract

Adversarial attack methods can significantly improve the classification accuracy of deep learning models, but research has found that although most deep learning models with defense methods still show good classification accuracy in the face of various adversarial attack attacks, the improved robust models have a significantly lower classification accuracy when facing clean samples compared to themselves without using defense methods. This means that while improving the model’s adversarial robustness, it is necessary to find a defense method to balance the accuracy of clean samples (clean accuracy) and the accuracy of adversarial samples (robust accuracy). Therefore, in this work, we propose an Adaptive Asynchronous Generalized Adversarial Training (A3GT) method, which is an improvement over the existing Generalist method. It employs an adaptive update strategy without the need for extensive experiments to determine the optimal starting iteration for global updates. The experimental results show that compared with other advanced methods, A3GT can achieve a balance between clean sample classification accuracy and robust classification accuracy while improving the model’s adversarial robustness.