Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences Article Swipe
Hua Mu
,
Chenggang Li
,
Anjie Peng
,
Yangyang Wang
,
Zhenyu Liang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25061770
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25061770
The threat posed by adversarial examples (AEs) to deep learning applications has garnered significant attention from the academic community. In response, various defense strategies have been proposed, including adversarial example detection. A range of detection algorithms has been developed to differentiate between benign samples and adversarial examples. However, the detection accuracy of these algorithms is significantly influenced by the characteristics of the adversarial attacks, such as attack type and intensity. Furthermore, the impact of image preprocessing on detection robustness—a common step before adversarial example generation—has been largely overlooked in prior research. To address these challenges, this paper introduces a novel adversarial example detection algorithm based on high-level feature differences (HFDs), which is specifically designed to improve robustness against both attacks and preprocessing operations. For each test image, a counterpart image with the same predicted label is randomly selected from the training dataset. The high-level features of both images are extracted using an encoder and compared through a similarity measurement model. If the feature similarity is low, the test image is classified as an adversarial example. The proposed method was evaluated for detection accuracy against four comparison methods, showing significant improvements over FS, DF, and MD, with a performance comparable to ESRM. Therefore, the subsequent robustness experiments focused exclusively on ESRM. Our results demonstrate that the proposed method exhibits superior robustness against preprocessing operations, such as downsampling and common corruptions, applied by attackers before generating adversarial examples. It is also applicable to various target models. By exploiting semantic conflicts in high-level features between clean and adversarial examples with the same predicted label, the method achieves high detection accuracy across diverse attack types while maintaining resilience to preprocessing, providing a valuable new perspective in the design of adversarial example detection algorithms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25061770
- https://www.mdpi.com/1424-8220/25/6/1770/pdf?version=1741793910
- OA Status
- gold
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408415355
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408415355Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s25061770Digital Object Identifier
- Title
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Robust Adversarial Example Detection Algorithm Based on High-Level Feature DifferencesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-03-12Full publication date if available
- Authors
-
Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang, Zhenyu LiangList of authors in order
- Landing page
-
https://doi.org/10.3390/s25061770Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/25/6/1770/pdf?version=1741793910Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/25/6/1770/pdf?version=1741793910Direct OA link when available
- Concepts
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Adversarial system, Robustness (evolution), Computer science, Preprocessor, Artificial intelligence, Upsampling, Pattern recognition (psychology), Machine learning, Data mining, Feature extraction, Image (mathematics), Algorithm, Gene, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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