Jingjing Rao and Tetsutaro Uehara, Ritsumeikan University, Japan

Since the development of deep learning technology, various new technologies have emerged one after another, greatly facilitating our daily lives. However, the development of these technologies has also brought some troubles, among which Deepfake technology is a typical example. Deepfake technology is mainly used to generate false pictures and videos, or modify real pictures and videos to achieve the purpose of deception. In the early days of this technology, people could often distinguish the authenticity with the naked eye. However, as the technology matures, the generated pictures and videos become more and more realistic, and many criminals have begun to use this technology to commit economic fraud, produce illegal pornographic content, distort political facts and other illegal acts. In order to better understand the importance of Deepfake detection and its related technologies, this article sorts out the main Deepfake detection technologies from 2018 to 2024. We briefly explain the various methods mentioned in the work and organize them into a table form. At the same time, we also set up a series of Q&A sessions, the purpose of which is to comprehensively introduce Deepfake technology and its detection methods from multiple perspectives, so as to help readers fully understand the latest developments and challenges in this field.

Deepfake, Detection, State-of-the-Art,GANs, Deeplearning, Dataset, Traditional method