Deepfake detection using deep learning methods: A systematic and comprehensive review

作者：Arash Heidari, Nima Jafari Navimipour, Hasan Dağ, Mehmet Ünal · 发表于：Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 年份：2023 · DOI：10.1002/widm.1520 · 研究领域：Advanced Technologies in Various Fields、Smart Systems and Machine Learning、AI and Big Data Applications

Abstract Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL‐based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how ...