  Adversarial sample attacks perturb benign inputs to induce DNN misbehaviors.
Recent research has demonstrated the widespread presence and the devastating
consequences of such attacks. Existing defense techniques either assume prior
knowledge of specific attacks or may not work well on complex models due to
their underlying assumptions. We argue that adversarial sample attacks are
deeply entangled with interpretability of DNN models: while classification
results on benign inputs can be reasoned based on the human perceptible
features/attributes, results on adversarial samples can hardly be explained.
Therefore, we propose a novel adversarial sample detection technique for face
recognition models, based on interpretability. It features a novel
bi-directional correspondence inference between attributes and internal neurons
to identify neurons critical for individual attributes. The activation values
of critical neurons are enhanced to amplify the reasoning part of the
computation and the values of other neurons are weakened to suppress the
uninterpretable part. The classification results after such transformation are
compared with those of the original model to detect adversaries. Results show
that our technique can achieve 94% detection accuracy for 7 different kinds of
attacks with 9.91% false positives on benign inputs. In contrast, a
state-of-the-art feature squeezing technique can only achieve 55% accuracy with
23.3% false positives.

Machine Learning;Artificial Intelligence;Cryptography and Security;Machine Learning