A survey of adversarial attacks and defenses on visual perception in automatic driving

• Published in: Nanjing Xinxi Gongcheng Daxue Xuebao Vol. 11; no. 6; pp. 651 - 659
• Main Authors: Yang, Yijun, Shao, Wenze, Wang, Liqian, Ge, Qi, Bao, Bingkun, Deng, Haisong, Li, Haibo
• Format: Journal Article
• Language: Chinese
• Published: Nanjing Nanjing University of Information Science & Technology 01.12.2019
• Subjects: Algorithms; Computer vision; Cross cutting; Deep learning; Image detection; Machine learning; Object recognition; Perception; Target detection; Target recognition; Visual perception; Voice recognition
• ISSN: 1674-7070
• DOI: 10.13878/j.cnki.jnuist.2019.06.003

Nowadays,deep learning has become one of the hottest research directions in the field of machine learning.it has achieved great success in a wide range of fields such as image recognition,target detection,voice processing,and question answering system.However,the emergence of adversarial examples has triggered new thinking on deep learning.The performance of deep learning models can be destroyed by adversarial examples constructed by adding specially designed subtle disturbance.The existence of adversarial examples makes many technical fields with high requirements on safety performance face new threats and challenges, especially the automatic driving system which uses visual perception as the main technology priority.Therefore,the research on adversarial attack and active defense has become an extremely important cross-cutting research topic in the field of deep learning and computer vision.in this paper,relevant concepts on adversarial examples are summarized firstly,and then a series of typical adversarial