  Adversarial attacks modify images with perturbations that change the
prediction of classifiers. These modified images, known as adversarial
examples, expose the vulnerabilities of deep neural network classifiers. In
this paper, we investigate the predictability of the mapping between the
classes predicted for original images and for their corresponding adversarial
examples. This predictability relates to the possibility of retrieving the
original predictions and hence reversing the induced misclassification. We
refer to this property as the reversibility of an adversarial attack, and
quantify reversibility as the accuracy in retrieving the original class or the
true class of an adversarial example. We present an approach that reverses the
effect of an adversarial attack on a classifier using a prior set of
classification results. We analyse the reversibility of state-of-the-art
adversarial attacks on benchmark classifiers and discuss the factors that
affect the reversibility.

Machine Learning;Artificial Intelligence;Cryptography and Security