  Deep neural networks (DNNs) are powerful nonlinear architectures that are
known to be robust to random perturbations of the input. However, these models
are vulnerable to adversarial perturbations--small input changes crafted
explicitly to fool the model. In this paper, we ask whether a DNN can
distinguish adversarial samples from their normal and noisy counterparts. We
investigate model confidence on adversarial samples by looking at Bayesian
uncertainty estimates, available in dropout neural networks, and by performing
density estimation in the subspace of deep features learned by the model. The
result is a method for implicit adversarial detection that is oblivious to the
attack algorithm. We evaluate this method on a variety of standard datasets
including MNIST and CIFAR-10 and show that it generalizes well across different
architectures and attacks. Our findings report that 85-93% ROC-AUC can be
achieved on a number of standard classification tasks with a negative class
that consists of both normal and noisy samples.
