Enhancing adversarial robustness of deep neural networks

Abstract

Logit-based regularization and pretrain-then-tune are two approaches that have recently been shown to enhance adversarial robustness of machine learning models. In the realm of regularization, Zhang et al. (2019) proposed TRADES, a logit-based regularization optimization function that has been shown to improve upon the robust optimization framework developed by Madry et al. (2018) [14, 9]. They were able to achieve state-of-the-art adversarial accuracy on CIFAR10. In the realm of pretrain- then-tune models, Hendrycks el al. (2019) demonstrated that adversarially pretraining a model on ImageNet then adversarially tuning on CIFAR10 greatly improves the adversarial robustness of machine learning models. In this work, we propose Adversarial Regularization, another logit-based regularization optimization framework that surpasses TRADES in adversarial generalization. Furthermore, we explore the impact of trying different types of adversarial training on the pretrain-then-tune paradigm.