Survey of generation,attack and defense of adversarial examples

Liu Xiaoleia,b
Luo Yuhengb
Shao Linb
Zhang Xiaosongb
Zhu Qingxina
a. School of Information & Software Engineering, b. Center for Cyber Security, University of Electronic Science & Technology of China, Chengdu 611731, China

Abstract

Attack methods based on adversarial samples are one of the security challenges that machine learning algorithms are commonly facing. This paper took the security of machine learning as a starting point, introduced the current security issues such as privacy attack and integrity attack that were faced by machine learning, summarized the development process and respective characteristics of current adversarial sample generation methods, and summarized the existing defense techniques for adversarial sample attacks, finally made a further look at how to improve the robustness of machine learning algorithms.

Foundation Support

国家自然科学基金资助项目(61572115)
四川省苗子工程创新基金资助项目(2019JDRC0069)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.07.0252
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 11
Section: Survey
Pages: 3201-3205,3212
Serial Number: 1001-3695(2020)11-001-3201-05

Publish History

[2020-11-05] Printed Article

Cite This Article

刘小垒, 罗宇恒, 邵林, 等. 对抗样本生成及攻防技术研究 [J]. 计算机应用研究, 2020, 37 (11): 3201-3205,3212. (Liu Xiaolei, Luo Yuheng, Shao Lin, et al. Survey of generation,attack and defense of adversarial examples [J]. Application Research of Computers, 2020, 37 (11): 3201-3205,3212. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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