Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients | |
Cheng-Cheng Ma2,3; Bao-Yuan Wu1,4; Yan-Bo Fan5; Yong Zhang5; Zhi-Feng Li5 | |
刊名 | Machine Intelligence Research |
2023 | |
卷号 | 20期号:5页码:666-682 |
关键词 | Adversarial defense, adversarial detection, generalized Gaussian distribution, Benford-Fourier coefficients, image classification |
ISSN号 | 2731-538X |
DOI | 10.1007/s11633-022-1328-1 |
英文摘要 | Adversarial example has been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD) but with different parameters (i.e., shape factor, mean, and variance). GGD is a general distribution family that covers many popular distributions (e.g., Laplacian, Gaussian, or uniform). Therefore, it is more likely to approximate the intrinsic distributions of internal responses than any specific distribution. Besides, since the shape factor is more robust to different databases rather than the other two parameters, we propose to construct discriminative features via the shape factor for adversarial detection, employing the magnitude of Benford-Fourier (MBF) coefficients, which can be easily estimated using responses. Finally, a support vector machine is trained as an adversarial detector leveraging the MBF features. Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust in detecting adversarial examples of different crafting methods and sources compared to state-of-the-art adversarial detection methods. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/56002] |
专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
作者单位 | 1.Shenzhen Research Institute of Big Data, Shenzhen 518172, China 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 4.School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China 5.AI Lab, Tencent Inc., Shenzhen 518057, China |
推荐引用方式 GB/T 7714 | Cheng-Cheng Ma,Bao-Yuan Wu,Yan-Bo Fan,et al. Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients[J]. Machine Intelligence Research,2023,20(5):666-682. |
APA | Cheng-Cheng Ma,Bao-Yuan Wu,Yan-Bo Fan,Yong Zhang,& Zhi-Feng Li.(2023).Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients.Machine Intelligence Research,20(5),666-682. |
MLA | Cheng-Cheng Ma,et al."Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients".Machine Intelligence Research 20.5(2023):666-682. |
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