Detection based Defense against Adversarial Examples from the Steganalysis Point of View
This addresses the problem of adversarial attacks on DNN-based systems, offering a defense that resists secondary attacks, though it is incremental as it builds on existing steganalysis and detection approaches.
The paper tackles the vulnerability of Deep Neural Networks to adversarial examples by proposing a detection-based defense method using steganalysis features enhanced with probability estimation of modifications, achieving accurate detection as shown in experiments.
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.