  Unsupervised representation learning leverages large unlabeled datasets and
is competitive with supervised learning. But non-robust encoders may affect
downstream task robustness. Recently, robust representation encoders have
become of interest. Still, all prior work evaluates robustness using a
downstream classification task. Instead, we propose a family of unsupervised
robustness measures, which are model- and task-agnostic and label-free. We
benchmark state-of-the-art representation encoders and show that none dominates
the rest. We offer unsupervised extensions to the FGSM and PGD attacks. When
used in adversarial training, they improve most unsupervised robustness
measures, including certified robustness. We validate our results against a
linear probe and show that, for MOCOv2, adversarial training results in 3 times
higher certified accuracy, a 2-fold decrease in impersonation attack success
rate and considerable improvements in certified robustness.
