  Deep neural networks (DNNs) have been shown to be vulnerable against
adversarial examples (AEs) which are maliciously designed to fool target
models. The normal examples (NEs) added with imperceptible adversarial
perturbation, can be a security threat to DNNs. Although the existing AEs
detection methods have achieved a high accuracy, they failed to exploit the
information of the AEs detected. Thus, based on high-dimension perturbation
extraction, we propose a model-free AEs detection method, the whole process of
which is free from querying the victim model. Research shows that DNNs are
sensitive to the high-dimension features. The adversarial perturbation hiding
in the adversarial example belongs to the high-dimension feature which is
highly predictive and non-robust. DNNs learn more details from high-dimension
data than others. In our method, the perturbation extractor can extract the
adversarial perturbation from AEs as high-dimension feature, then the trained
AEs discriminator determines whether the input is an AE. Experimental results
show that the proposed method can not only detect the adversarial examples with
high accuracy, but also detect the specific category of the AEs. Meanwhile, the
extracted perturbation can be used to recover the AEs to NEs.
