A Survey on Deepfake Detection Technologies

With the rapid development of artificial intelligence technology, deepfake technology has made significant advancements in recent years, achieving unprecedented levels of visual realism and voice mimicry in generated fake content. The misuse of this technology poses serious threats to social security, personal privacy, and information authenticity. This paper systematically reviews the latest research progress in deepfake detection technology, covering traditional methods to modern detection techniques based on deep learning. We first introduce the basic principles and classification of deepfake technology, then analyze in detail major technical approaches including physical feature detection, deep learning detection, large model-based detection methods, and biometric detection. Through analysis of extensive research literature, this paper focuses on the technical characteristics, application scenarios, and performance of various detection methods. Meanwhile, we also conduct an in-depth discussion of challenges facing current detection technologies, including adversarial sample problems, limitations of large model detection, and future research directions. This survey aims to provide researchers with a comprehensive technical reference framework to promote further development of deepfake detection technology.