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 output distribution of 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. In specific, we study the distributional difference of
hidden layer output between natural and adversarial examples, and propose to
use the randomness of Bayesian neural network (BNN) to simulate hidden layer
output distribution and leverage the distribution dispersion to detect
adversarial examples. The advantage of BNN is that the output is stochastic
while neural networks without random components do 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.

这篇论文提出了一种新的框架，即 BATer，使用贝叶斯神经网络（BNN）来模拟隐藏层输出分布的随机性和分散度，从而提高对抗性例子的检测性能，实验结果表明，与当前最先进的检测器相比，BATer 在对抗性例子检测方面表现更好。