  Deep neural networks are known to be vulnerable to adversarial examples,
where a perturbation in the input space leads to an amplified shift in the
latent network representation. In this paper, we combine canonical supervised
learning with self-supervised representation learning, and present
Self-supervised Online Adversarial Purification (SOAP), a novel defense
strategy that uses a self-supervised loss to purify adversarial examples at
test-time. Our approach leverages the label-independent nature of
self-supervised signals and counters the adversarial perturbation with respect
to the self-supervised tasks. SOAP yields competitive robust accuracy against
state-of-the-art adversarial training and purification methods, with
considerably less training complexity. In addition, our approach is robust even
when adversaries are given knowledge of the purification defense strategy. To
the best of our knowledge, our paper is the first that generalizes the idea of
using self-supervised signals to perform online test-time purification.
