In the era of big data,neural network-based model research is a mainstream direction in the field of artificial intelligence.Compared with other intelligent optimization algorithms,neural network has the advantages of strong adapta-bility and significant generalization ability,and is widely used in the fields of speech recognition,computer vision and natural language processing.However,as neural network plays a key role in various fields,it also causes privacy security problems such as privacy leakage and data theft.Artificial intelligence security has become a hot topic at home and abroad.Model inversion attack technique based on neural network studies how to learn and derive from the output data of neural network models to obtain information about the input data.Through in-depth mining and association analysis of the input data,important sensitive data of users may be restored,leading to more serious security problems.At the same time,the model inversion attack technology can also deduce the information about the network structure and model parameters of the neural network,which will threaten the security of the neural network model.In order to systematically understand the research progress and present situation of model inversion attack technology based on neural network,this paper makes a detailed investigation on the security problems of neural network and model inversion attack technology.Firstly,this paper introduces the concept of model inversion attack technology and common attack scenarios.Then,the challenges of model inversion attacks faced by neural networks are discussed,including original data protection,sensitive data leakage,model training privacy and other security issues.Then,two kinds of neural network model inversion attack techniques based on gradient optimization and parameter training are reviewed,various methods are compared,and the typical defense meth-ods are summarized.Finally,the paper summarizes the whole paper and discusses the future research direction.
维普
万方数据