  Recent studies have found that deep learning systems are vulnerable to
adversarial examples; e.g., visually unrecognizable adversarial images can
easily be crafted to result in misclassification. The robustness of neural
networks has been studied extensively in the context of adversary detection,
which compares a metric that exhibits strong discriminate power between natural
and adversarial examples. In this paper, we propose to characterize the
adversarial subspaces through the lens of mutual information (MI) approximated
by conditional generation methods. We use MI as an information-theoretic metric
to strengthen existing defenses and improve the performance of adversary
detection. Experimental results on MagNet defense demonstrate that our proposed
MI detector can strengthen its robustness against powerful adversarial attacks.
