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- Title
Adversarial Detection Based on Inner-Class Adjusted Cosine Similarity †.
- Authors
Guan, Dejian; Zhao, Wentao
- Abstract
Deep neural networks (DNNs) have attracted extensive attention because of their excellent performance in many areas; however, DNNs are vulnerable to adversarial examples. In this paper, we propose a similarity metric called inner-class adjusted cosine similarity (IACS) and apply it to detect adversarial examples. Motivated by the fast gradient sign method (FGSM), we propose to utilize an adjusted cosine similarity which takes both the feature angle and scale information into consideration and therefore is able to effectively discriminate subtle differences. Given the predicted label, the proposed IACS is measured between the features of the test sample and those of the normal samples with the same label. Unlike other detection methods, we can extend our method to extract disentangled features with different deep network models but are not limited to the target model (the adversarial attack model). Furthermore, the proposed method is able to detect adversarial examples crossing attacks, that is, a detector learned with one type of attack can effectively detect other types. Extensive experimental results show that the proposed IACS features can well distinguish adversarial examples and normal examples and achieve state-of-the-art performance.
- Subjects
ARTIFICIAL neural networks
- Publication
Applied Sciences (2076-3417), 2022, Vol 12, Issue 19, p9406
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app12199406