Review of Artificial Intelligence Adversarial Attack and Defense Technologies

被引:240
作者
Qiu, Shilin [1 ,2 ]
Liu, Qihe [1 ,2 ]
Zhou, Shijie [1 ,2 ]
Wu, Chunjiang [1 ,2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China
[2] 4,Sect 2,Jianshe North Rd, Chengdu 610054, Sichuan, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 05期
关键词
artificial intelligence; deep learning; adversarial sample; adversarial attack; defense method; VISION;
D O I
10.3390/app9050909
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model's different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools.
引用
收藏
页数:29
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