  Deep Neural Networks (DNNs) have been shown vulnerable to Test-Time Evasion
attacks (TTEs, or adversarial examples), which, by making small changes to the
input, alter the DNN's decision. We propose an unsupervised attack detector on
DNN classifiers based on class-conditional Generative Adversarial Networks
(GANs). We model the distribution of clean data conditioned on the predicted
class label by an Auxiliary Classifier GAN (AC-GAN). Given a test sample and
its predicted class, three detection statistics are calculated based on the
AC-GAN Generator and Discriminator. Experiments on image classification
datasets under various TTE attacks show that our method outperforms previous
detection methods. We also investigate the effectiveness of anomaly detection
using different DNN layers (input features or internal-layer features) and
demonstrate, as one might expect, that anomalies are harder to detect using
features closer to the DNN's output layer.
