  Despite the impressive performances reported by deep neural networks in
different application domains, they remain largely vulnerable to adversarial
examples, i.e., input samples that are carefully perturbed to cause
misclassification at test time. In this work, we propose a deep neural
rejection mechanism to detect adversarial examples, based on the idea of
rejecting samples that exhibit anomalous feature representations at different
network layers. With respect to competing approaches, our method does not
require generating adversarial examples at training time, and it is less
computationally demanding. To properly evaluate our method, we define an
adaptive white-box attack that is aware of the defense mechanism and aims to
bypass it. Under this worst-case setting, we empirically show that our approach
outperforms previously-proposed methods that detect adversarial examples by
only analyzing the feature representation provided by the output network layer.
