Adversarial Training with Bi-directional Likelihood Regularization for Visual Classification

Abstract

Neural networks are vulnerable to adversarial attacks. Practically, adversarial training is by far the most effective approach for enhancing the robustness of neural networks against adversarial examples. The current adversarial training approach aims to maximize the posterior probability for adversarially perturbed training data. However, such a training strategy ignores the fact that the clean data and adversarial examples should have intrinsically different feature distributions despite that they are assigned with the same class label under adversarial training. We propose that this problem can be solved by explicitly modeling the deep feature distribution, for example as a Gaussian Mixture, and then properly introducing the likelihood regularization into the loss function. Specifically, by maximizing the likelihood of features of clean data and minimizing that of adversarial examples simultaneously, the neural network learns a more reasonable feature distribution in which the intrinsic difference between clean data and adversarial examples can be explicitly preserved. We call such a new robust training strategy the adversarial training with bi-directional likelihood regularization (ATBLR) method. Extensive experiments on various datasets demonstrate that the ATBLR method facilitates robust classification of both clean data and adversarial examples, and performs favorably against previous state-of-the-art methods for robust visual classification.