Abstract
In recent years, deep learning technology has been exploited to create fake videos, leading to the widespread presence of deepfake content on the Internet. This technology facilitates the production of counterfeit content, including pornographic films, fabricated news, and political misinformation, by altering or substituting the facial data, expressions, and body movements in original videos, as well as synthesizing voices of specific individuals. Various generative tools, such as generative adversarial networks (GANs), are employed in deep forgery, introducing significant risk challenges. Detection technology serves as a crucial countermeasure to mitigate the adverse effects, with methods based on spatial and frequency domain information playing a pivotal role. This paper delves into the generative tools and detection techniques of deepfake technology, examining their principles and applications, and thoroughly discusses the risk challenges encountered. The aim is to furnish references for further research and practical measures, enhancing understanding and management of this technology and its impacts, thereby fostering the advancement and refinement of the field.
Published Version
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