  Deep Neural Networks (DNNs) have recently made significant progress in many
fields. However, studies have shown that DNNs are vulnerable to adversarial
examples, where imperceptible perturbations can greatly mislead DNNs even if
the full underlying model parameters are not accessible. Various defense
methods have been proposed, such as feature compression and gradient masking.
However, numerous studies have proven that previous methods create detection or
defense against certain attacks, which renders the method ineffective in the
face of the latest unknown attack methods. The invisibility of adversarial
perturbations is one of the evaluation indicators for adversarial example
attacks, which also means that the difference in the local correlation of
high-frequency information in adversarial examples and normal examples can be
used as an effective feature to distinguish the two. Therefore, we propose an
adversarial example detection framework based on a high-frequency information
enhancement strategy, which can effectively extract and amplify the feature
differences between adversarial examples and normal examples. Experimental
results show that the feature augmentation module can be combined with existing
detection models in a modular way under this framework. Improve the detector's
performance and reduce the deployment cost without modifying the existing
detection model.
