A Review of Deepfake Detection Techniques
It is argued that the future deepfake detection algorithm will be more accurate, which can further maintain the authenticity of network information and social stability.
Abstract
With the development of deepfake technology, the use of this technology to forge videos and images has caused serious privacy and legal problems in society. In order to solve these problems, deepfake detection is required. In this paper, the generation and detection techniques of deepfakes in recent years are studied. First, the principles of deepfake generation technology are briefly introduced, including Generative Adversarial Networks (GAN) based and autoencoder. Then, this paper focuses on the detection techniques of deepfakes, classifies them based on the principles of each method, and summarizes the advantages and limitations of each method. At the end of the paper, several key points for the future development of deepfake detection technology are proposed: enhancing the generalization ability and robustness of deepfake detection methods, developing active defensive algorithms and multimodal fusion detection, establishing research communities and data sharing platforms, and improving social legislation and judicial education. This paper argues that the future deepfake detection algorithm will be more accurate, which can further maintain the authenticity of network information and social stability.