Transferring Adversarial Robustness Through Robust Representation Matching. (arXiv:2202.09994v2 [cs.LG] UPDATED)

With the widespread use of machine learning, concerns over its security and
reliability have become prevalent. As such, many have developed defenses to
harden neural networks against adversarial examples, imperceptibly perturbed
inputs that are reliably misclassified. Adversarial training in which
adversarial examples are generated and used during training is one of the few
known defenses able to reliably withstand such attacks against neural networks.
However, adversarial training imposes a significant training overhead and
scales poorly with model complexity and input …

adversarial lg representation