  Though deep neural network has hit a huge success in recent studies and
applica- tions, it still remains vulnerable to adversarial perturbations which
are imperceptible to humans. To address this problem, we propose a novel
network called ReabsNet to achieve high classification accuracy in the face of
various attacks. The approach is to augment an existing classification network
with a guardian network to detect if a sample is natural or has been
adversarially perturbed. Critically, instead of simply rejecting adversarial
examples, we revise them to get their true labels. We exploit the observation
that a sample containing adversarial perturbations has a possibility of
returning to its true class after revision. We demonstrate that our ReabsNet
outperforms the state-of-the-art defense method under various adversarial
attacks.
