Research on Fake Data Identification and Defense Mechanism Based on Deep Learning

With the rapid development of the Internet and big data, a large amount of fake data and fake information widely exist in many industries such as finance, healthcare, and social networks. This project conducts in - depth research on the detection and protection mechanism of fake data. It aims to use deep learning technology to build new modeling and protection methods, and improve the identification and defense mechanism of fake data. Firstly, the concept and classification of fake data are elaborated, and the application prospects of fake data in the financial, medical, and social industries are analyzed. On this basis, the classical algorithms based on machine learning are combined with deep learning algorithms to study their advantages and disadvantages in fake data detection. Then, a new network security defense mechanism is constructed, and its learning process and solution methods are described in detail. Finally, this project will test the network security protection system and the security defense mechanism established in this project, and conduct a performance analysis of its effects. The research of this project will provide new ways and methods for fake data detection and protection, and has certain reference significance for follow - up research.