梁瑞刚,吕培卓,赵月,陈鹏,邢豪,张颖君,韩冀中,赫然,赵险峰,李明,陈恺.视听觉深度伪造检测技术研究综述[J].信息安全学报,2020,5(2):1-17 [ 点击复制 ]

LIANG Ruigang,LV Peizhuo,ZHAO Yue,CHEN Peng,XING Hao,ZHANG Yingjun,HAN Jizhong,He Ran,ZHAO Xianfeng,LI Ming,CHEN Kai.A Survey of Audiovisual Deepfake Detection Techniques[J].Journal of Cyber Security,2020,5(2):1-17 [ 点击复制 ]

(1.State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China;2.School of Cyber Security, University of Chinese Academy of Science, Beijing 100049, China;3.College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China;4.Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;5.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China) Abstract : Deep learning has been widely used in fields such as natural language processing, computer vision, and driverless vehicles, leading a new wave of artificial intelligence. Deep learning advances however have also been used to create technologies that pose potential threats to national security, social stability, and personal privacy. For example, deepfakes that have recently attracted widespread attention worldwide, which could generate seemingly realistic fake images, audio and video content. This article introduces the background of deepfakes and the principles of deepfakes creation, and then outlines and analyzes the detection methods and datasets for different types of deepfakes, including images, videos, audios, etc. Finally, the article discusses potential research directions and challenges of deepfakes detection and prevention. Key words : deepfakes deep learning generative adversarial network 京公网安备11010802043679号
主办：中国科学院信息工程研究所、中国科技出版传媒股份有限公司