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	Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
	Han Xu; Yao Ma; Hao-Chen Liu; Debayan Deb; Hui Liu; Ji-Liang Tang; Anil K. Jain
刊名	International Journal of Automation and Computing
	2020
卷号	17期号:2页码:151-178
关键词	Adversarial example model safety robustness defenses deep learning.
ISSN号	1476-8186
DOI	10.1007/s11633-019-1211-x
英文摘要	Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples raises our concerns in adopting deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for three most popular data types, including images, graphs and text.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42295]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	Department of Computer Science and Engineering, Michigan State University, Michigan 48823, USA
推荐引用方式 GB/T 7714	Han Xu,Yao Ma,Hao-Chen Liu,et al. Adversarial Attacks and Defenses in Images, Graphs and Text: A Review[J]. International Journal of Automation and Computing,2020,17(2):151-178.
APA	Han Xu.,Yao Ma.,Hao-Chen Liu.,Debayan Deb.,Hui Liu.,...&Anil K. Jain.(2020).Adversarial Attacks and Defenses in Images, Graphs and Text: A Review.International Journal of Automation and Computing,17(2),151-178.
MLA	Han Xu,et al."Adversarial Attacks and Defenses in Images, Graphs and Text: A Review".International Journal of Automation and Computing 17.2(2020):151-178.

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