| Title |
Adversarial Attack and Defense: A Survey |
| ID_Doc |
42553 |
| Authors |
Liang, HS; He, EL; Zhao, YY; Jia, Z; Li, H |
| Title |
Adversarial Attack and Defense: A Survey |
| Year |
2022 |
| Published |
Electronics, 11, 8 |
| DOI |
10.3390/electronics11081283 |
| Abstract |
In recent years, artificial intelligence technology represented by deep learning has achieved remarkable results in image recognition, semantic analysis, natural language processing and other fields. In particular, deep neural networks have been widely used in different security-sensitive tasks. Fields, such as facial payment, smart medical and autonomous driving, which accelerate the construction of smart cities. Meanwhile, in order to fully unleash the potential of edge big data, there is an urgent need to push the AI frontier to the network edge. Edge AI, the combination of artificial intelligence and edge computing, supports the deployment of deep learning algorithms to edge devices that generate data, and has become a key driver of smart city development. However, the latest research shows that deep neural networks are vulnerable to attacks from adversarial example and output wrong results. This type of attack is called adversarial attack, which greatly limits the promotion of deep neural networks in tasks with extremely high security requirements. Due to the influence of adversarial attacks, researchers have also begun to pay attention to the research in the field of adversarial defense. In the game process of adversarial attacks and defense technologies, both attack and defense technologies have been developed rapidly. This article first introduces the principles and characteristics of adversarial attacks, and summarizes and analyzes the adversarial example generation methods in recent years. Then, it introduces the adversarial example defense technology in detail from the three directions of model, data, and additional network. Finally, combined with the current status of adversarial example generation and defense technology development, put forward challenges and prospects in this field. |
| Author Keywords |
adversarial example; deep neural network; smart city; adversarial defense; black-box attack; white-box attack |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000786145600001 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied |
| Research Area |
Computer Science; Engineering; Physics |
| PDF |
https://www.mdpi.com/2079-9292/11/8/1283/pdf?version=1650278650
|