TY - JOUR
T1 - A Survey on Adversarial Attack in the Age of Artificial Intelligence
AU - Kong, Zixiao
AU - Xue, Jingfeng
AU - Wang, Yong
AU - Huang, Lu
AU - Niu, Zequn
AU - Li, Feng
N1 - Publisher Copyright:
© 2021 Zixiao Kong et al.
PY - 2021
Y1 - 2021
N2 - With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. An increasing number of researchers are working in this field. We provide a comprehensive review of the theories and methods that enable researchers to enter the field of adversarial attack. This article is according to the "Why? → What? → How?"research line for elaboration. Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. Finally, we review the typical attack algorithms and defense techniques in each application area. Facing the increasingly complex neural network model, this paper focuses on the fields of image, text, and malicious code and focuses on the adversarial attack classifications and methods of these three data types, so that researchers can quickly find their own type of study. At the end of this review, we also raised some discussions and open issues and compared them with other similar reviews.
AB - With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. An increasing number of researchers are working in this field. We provide a comprehensive review of the theories and methods that enable researchers to enter the field of adversarial attack. This article is according to the "Why? → What? → How?"research line for elaboration. Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. Finally, we review the typical attack algorithms and defense techniques in each application area. Facing the increasingly complex neural network model, this paper focuses on the fields of image, text, and malicious code and focuses on the adversarial attack classifications and methods of these three data types, so that researchers can quickly find their own type of study. At the end of this review, we also raised some discussions and open issues and compared them with other similar reviews.
UR - http://www.scopus.com/inward/record.url?scp=85109217318&partnerID=8YFLogxK
U2 - 10.1155/2021/4907754
DO - 10.1155/2021/4907754
M3 - Article
AN - SCOPUS:85109217318
SN - 1530-8669
VL - 2021
JO - Wireless Communications and Mobile Computing
JF - Wireless Communications and Mobile Computing
M1 - 4907754
ER -