Adversarial Perturbation Defense on Deep Neural Networks

doi:10.1145/3465397

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Adversarial Perturbation Defense on Deep Neural Networks
	Zhang, Xingwei; Zheng, Xiaolong; Mao, Wenji
刊名	ACM COMPUTING SURVEYS
	2021-11-01
卷号	54 期号:8 页码:36
关键词	Adversarial perturbation defense deep neural networks security origin
ISSN号	0360-0300
DOI	10.1145/3465397
通讯作者	Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
英文摘要	Deep neural networks (DNNs) have been verified to be easily attacked by well-designed adversarial perturbations. Image objects with small perturbations that are imperceptible to human eyes can induce DNN-based image class classifiers towards making erroneous predictions with high probability. Adversarial perturbations can also fool real-world machine learning systems and transfer between different architectures and datasets. Recently, defense methods against adversarial perturbations have become a hot topic and attracted much attention. A large number of works have been put forward to defend against adversarial perturbations, enhancing DNN robustness against potential attacks, or interpreting the origin of adversarial perturbations. In this article, we provide a comprehensive survey on classical and state-of-the-art defense methods by illuminating their main concepts, in-depth algorithms, and fundamental hypotheses regarding the origin of adversarial perturbations. In addition, we further discuss potential directions of this domain for future researchers.
资助项目	Ministry of Health of China[2017ZX10303401-002] ; Ministry of Health of China[2017YFC1200302] ; Ministry of Science and Technology of China[2020AAA0108401and 2019QY(Y)0101] ; Natural Science Foundation of China[71602184] ; Natural Science Foundation of China[71621002]
WOS关键词	EVASION ATTACKS ; ROBUSTNESS
WOS研究方向	Computer Science
语种	英语
出版者	ASSOC COMPUTING MACHINERY
WOS记录号	WOS:000705073600003
资助机构	Ministry of Health of China ; Ministry of Science and Technology of China ; Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46188]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Zheng, Xiaolong
作者单位	Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xingwei,Zheng, Xiaolong,Mao, Wenji. Adversarial Perturbation Defense on Deep Neural Networks[J]. ACM COMPUTING SURVEYS,2021,54(8):36.
APA	Zhang, Xingwei,Zheng, Xiaolong,&Mao, Wenji.(2021).Adversarial Perturbation Defense on Deep Neural Networks.ACM COMPUTING SURVEYS,54(8),36.
MLA	Zhang, Xingwei,et al."Adversarial Perturbation Defense on Deep Neural Networks".ACM COMPUTING SURVEYS 54.8(2021):36.