Abstract: As one of the representative technologies of the rapid development of artificial intelligence, deep neural network has been applied more and more widely, and the security problems brought by it have gradually attracted attention. Existing studies mainly focus on how to efficiently construct diverse adversarial samples to cheat deep neural network models, and how to detect adversarial samples and reinforce deep neural network models. However, with the development of deep neural network models increasingly relying on open-source data sets, pre-trained models, computing frameworks and other third-party resources, the risk of models being implanted into backdoors is increasing. Starting from each link of the life cycle of deep neural network models, this paper summarizes the related technologies and methods of backdoor implantation and detection of deep neural network models, compares and analyzes the main characteristics and applicable scenarios of different methods, and prospects the future development direction of related technologies.

Key words: deep neural network, backdoor implantation, backdoor detection, artificial intelligence