Towards a Unified View of Adversarial Training: A Contrastive Perspective

Abstract

Adversarial training (AT) has been an effective approach to build a defensive model against adversarial attacks. However, most researches on AT are conducted in a supervised learning manner where true labels of training data are required. To relax this issue, the unsupervised AT through self-supervised learning is developed. In particular, this study presents an unsupervised AT by exploiting the concept of instance discrimination in contrastive learning where the unsupervised learning is implemented but closely connected to a supervised scheme for discrimination or classification. By utilizing such an implicit and inherent connection, a unified view is addressed for a new unsupervised AT where the contrastive learning objective is consolidated and strengthened from a classification perspective. A unified framework for supervised and unsupervised AT is extended. In the experiments, this method achieves state-of-the-art results in adversarial training of image classifier under different settings.