  The easiness at which adversarial instances can be generated in deep neural
networks raises some fundamental questions on their functioning and concerns on
their use in critical systems. In this paper, we draw a connection between
over-generalization and adversaries: a possible cause of adversaries lies in
models designed to make decisions all over the input space, leading to
inappropriate high-confidence decisions in parts of the input space not
represented in the training set. We empirically show an augmented neural
network, which is not trained on any types of adversaries, can increase the
robustness by detecting black-box one-step adversaries, i.e. assimilated to
out-distribution samples, and making generation of white-box one-step
adversaries harder.
