  In this paper, we introduce a novel neural network training framework that
increases model's adversarial robustness to adversarial attacks while
maintaining high clean accuracy by combining contrastive learning (CL) with
adversarial training (AT). We propose to improve model robustness to
adversarial attacks by learning feature representations that are consistent
under both data augmentations and adversarial perturbations. We leverage
contrastive learning to improve adversarial robustness by considering an
adversarial example as another positive example, and aim to maximize the
similarity between random augmentations of data samples and their adversarial
example, while constantly updating the classification head in order to avoid a
cognitive dissociation between the classification head and the embedding space.
This dissociation is caused by the fact that CL updates the network up to the
embedding space, while freezing the classification head which is used to
generate new positive adversarial examples. We validate our method, Contrastive
Learning with Adversarial Features(CLAF), on the CIFAR-10 dataset on which it
outperforms both robust accuracy and clean accuracy over alternative supervised
and self-supervised adversarial learning methods.
