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An Adversarial Attack System for Face Recognition

Yuetian Wang, Chuanjing Zhang, Xuxin Liao, Xingang Wang, Zhaoquan Gu*

Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

* Corresponding Author: Zhaoquan Gu. Email: email

Journal on Artificial Intelligence 2021, 3(1), 1-8.


Deep neural networks (DNNs) are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers. Many related works show that DNNs are vulnerable to adversarial examples that are generated with subtle perturbation to original images in both digital domain and physical domain. As a most common application of DNNs, face recognition systems are likely to cause serious consequences if they are attacked by the adversarial examples. In this paper, we implement an adversarial attack system for face recognition in both digital domain that generates adversarial face images to fool the recognition system, and physical domain that generates customized glasses to fool the system when a person wears the glasses. Experiments show that our system attacks face recognition systems effectively. Furthermore, our system could misguide the recognition system to identify a person wearing the customized glasses as a certain target. We hope this research could help raise the attention of artificial intelligence security and promote building robust recognition systems.


Cite This Article

APA Style
Wang, Y., Zhang, C., Liao, X., Wang, X., Gu, Z. (2021). An adversarial attack system for face recognition. Journal on Artificial Intelligence, 3(1), 1-8.
Vancouver Style
Wang Y, Zhang C, Liao X, Wang X, Gu Z. An adversarial attack system for face recognition. J Artif Intell . 2021;3(1):1-8
IEEE Style
Y. Wang, C. Zhang, X. Liao, X. Wang, and Z. Gu "An Adversarial Attack System for Face Recognition," J. Artif. Intell. , vol. 3, no. 1, pp. 1-8. 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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