深度神经网络的后门攻击研究进展

In recent years,deep neural networks(DNNs) have developed rapidly,and their applications involve many fields,including auto autonomous driving,natural language processing,facial recognition and so on,which have brought a lot of convenience to people's life.However,the growth of DNNs has brought some security concerns.In recent years,DNNs have been shown to be vulnerable to backdoor attacks,mainly due to their low transparency and poor interpretability,allowing attackers to to swoop in.In this paper,the potential security and privacy risks in neural network applications are revealed by reviewing the research work related to neural network backdoor attacks,and the importance of research in the field of backdoor is emphasized.This paper first briefly introduces the threat model of neural network backdoor,then the neural network backdoor attack is divided into two categories:the backdoor attack based on poisoning and the backdoor attack without poisoning,and the poisoning attack can be subdivided into multiple categories.It aggregates available resources about backdoor attack,and analyzes the development of backdoor on neural network and the future development trend of backdoor attack is prospected.