Deep Neural Networks (DNNs) have recently led to significant improvements in
many fields. However, DNNs are vulnerable to adversarial examples which are
samples with imperceptible perturbations while dramatically misleading the
DNNs. Moreover, adversarial examples can be used to perform an attack on
various kinds of DNN based systems, even if the adversary has no access to the
underlying model. Many defense methods have been proposed, such as obfuscating
gradients of the networks or detecting adversarial examples. However it is
proved out that these defense methods are not effective or cannot resist
secondary adversarial attacks. In this paper, we point out that steganalysis
can be applied to adversarial examples detection, and propose a method to
enhance steganalysis features by estimating the probability of modifications
caused by adversarial attacks. Experimental results show that the proposed
method can accurately detect adversarial examples. Moreover, secondary
adversarial attacks cannot be directly performed to our method because our
method is not based on a neural network but based on high-dimensional
artificial features and FLD (Fisher Linear Discriminant) ensemble.

本研究提出了一种检测对抗性样例的方法，通过估计对抗性攻击造成的修改概率来增强隐写分析特征，实验证明该方法可以准确检测对抗性样例，并且没有基于神经网络，而是基于高维人工特征和 FLD (Fisher Linear Discriminant) 系列，因此不能直接进行二次对抗性攻击。