mlsanityData to 7 Oct 2026

Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization

arXiv 2305.04436 ↗First posted 8 May 2023cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 64 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

33 of 64 references.

References · 33

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21 more
  1. 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
  2. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  3. 2017MagNet: a Two-Pronged Defense against Adversarial Examplescited by 268
  4. 2017Detecting Adversarial Samples from Artifactscited by 244
  5. 2020Graph Convolutional Networks for Hyperspectral Image Classificationcited by 113
  6. 2017Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compressioncited by 86
  7. 2016Adversarial Examples Detection in Deep Networks with Convolutional Filter Statisticscited by 77
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  15. 2021Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domaincited by 29
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