  Detection and rejection of adversarial examples in security sensitive and
safety-critical systems using deep CNNs is essential. In this paper, we propose
an approach to augment CNNs with out-distribution learning in order to reduce
misclassification rate by rejecting adversarial examples. We empirically show
that our augmented CNNs can either reject or classify correctly most
adversarial examples generated using well-known methods ( >95% for MNIST and
>75% for CIFAR-10 on average). Furthermore, we achieve this without requiring
to train using any specific type of adversarial examples and without
sacrificing the accuracy of models on clean samples significantly (< 4%).
