  In this paper, we propose a new framework to detect adversarial examples
motivated by the observations that random components can improve the smoothness
of predictors and make it easier to simulate the output distribution of a deep
neural network. With these observations, we propose a novel Bayesian
adversarial example detector, short for BATer, to improve the performance of
adversarial example detection. Specifically, we study the distributional
difference of hidden layer output between natural and adversarial examples, and
propose to use the randomness of the Bayesian neural network to simulate hidden
layer output distribution and leverage the distribution dispersion to detect
adversarial examples. The advantage of a Bayesian neural network is that the
output is stochastic while a deep neural network without random components does
not have such characteristics. Empirical results on several benchmark datasets
against popular attacks show that the proposed BATer outperforms the
state-of-the-art detectors in adversarial example detection.
