  Recently, unsupervised adversarial training (AT) has been highlighted as a
means of achieving robustness in models without any label information. Previous
studies in unsupervised AT have mostly focused on implementing self-supervised
learning (SSL) frameworks, which maximize the instance-wise classification loss
to generate adversarial examples. However, we observe that simply maximizing
the self-supervised training loss with an untargeted adversarial attack often
results in generating ineffective adversaries that may not help improve the
robustness of the trained model, especially for non-contrastive SSL frameworks
without negative examples. To tackle this problem, we propose a novel positive
mining for targeted adversarial attack to generate effective adversaries for
adversarial SSL frameworks. Specifically, we introduce an algorithm that
selects the most confusing yet similar target example for a given instance
based on entropy and similarity, and subsequently perturbs the given instance
towards the selected target. Our method demonstrates significant enhancements
in robustness when applied to non-contrastive SSL frameworks, and less but
consistent robustness improvements with contrastive SSL frameworks, on the
benchmark datasets.
