Journal of Software:2021.32(2):496-518

(浙江大学 计算机科学与技术学院, 浙江 杭州 310007;阿里巴巴, 浙江 杭州 311121;浙江大学 计算机科学与技术学院, 浙江 杭州 310007;之江实验室, 浙江 杭州 310000;浙江工商大学 计算机与信息工程学院, 浙江 杭州 310018;浙江大学 控制科学与工程学院, 浙江 杭州 310007;复旦大学 计算机科学技术学院, 上海 201203;浙江大学 管理学院, 浙江 杭州 310007)
Survey on Deepfakes and Detection Techniques
LI Xu-Rong,JI Shou-Ling,WU Chun-Ming,LIU Zhen-Guang,DENG Shui-Guang,CHENG Peng,YANG Min,KONG Xiang-Wei
(College of Computer Science and Technology, Zhejiang University, Hangzhou 310007, China;Alibaba Group, Hangzhou 311121, China;College of Computer Science and Technology, Zhejiang University, Hangzhou 310007, China;Zhejiang Lab, Hangzhou 310000, China;College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China;College of Control Science and Engineering, Zhejiang University, Hangzhou 310007, China;College of Computer Science, Fudan University, Shanghai 201203, China;College of Management, Zhejiang University, Hangzhou 310007, China)
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Received:May 07, 2020    Revised:June 22, 2020
> 中文摘要: 深度学习在计算机视觉领域取得了重大成功,超越了众多传统的方法.然而近年来,深度学习技术被滥用在假视频的制作上,使得以Deepfakes为代表的伪造视频在网络上泛滥成灾.这种深度伪造技术通过篡改或替换原始视频的人脸信息,并合成虚假的语音来制作色情电影、虚假新闻、政治谣言等.为了消除此类伪造技术带来的负面影响,众多学者对假视频的鉴别进行了深入的研究,并提出一系列的检测方法来帮助机构或社区去识别此类伪造视频.尽管如此,目前的检测技术仍然存在依赖特定分布数据、特定压缩率等诸多的局限性,远远落后于假视频的生成技术.并且不同学者解决问题的角度不同,使用的数据集和评价指标均不统一.迄今为止,学术界对深度伪造与检测技术仍缺乏统一的认识,深度伪造和检测技术研究的体系架构尚不明确.回顾了深度伪造与检测技术的发展,并对现有研究工作进行了系统的总结和科学的归类.最后讨论了深度伪造技术蔓延带来的社会风险,分析了检测技术的诸多局限性,并探讨了检测技术面临的挑战和潜在研究方向,旨在为后续学者进一步推动深度伪造检测技术的发展和部署提供指导.
中文关键词: 深度学习  深度伪造  假视频  取证  检测技术
Abstract:Deep learning has achieved great success in the field of computer vision, surpassing many traditional methods. However, in recent years, deep learning technology has been abused in the production of fake videos, making fake videos represented by Deepfakes flooding on the Internet. This technique produces pornographic movies, fake news, political rumors by tampering or replacing the face information of the original videos and synthesizes fake speech. In order to eliminate the negative effects brought by such forgery technologies, many researchers have conducted in-depth research on the identification of fake videos and proposed a series of detection methods to help institutions or communities to identify such fake videos. Nevertheless, the current detection technology still has many limitations such as specific distribution data, specific compression ratio, and so on, far behind the generation technology of fake video. In addition, different researchers handle the problem from different angles. The data sets and evaluation indicators used are not uniform. So far, the academic community still lacks a unified understanding of deep forgery and detection technology. The architecture of deep forgery and detection technology research is not clear. In this review, the development of deep forgery and detection technologies are reviewed. Besides, existing research works are systematically summarize and scientifically classified. Finally, the social risks posed by the spread of Deepfakes technology are discussed, the limitations of detection technology are analyzed, and the challenges and potential research directions of detection technology are discussed, aiming to provide guidance for follow-up researchers to further promote the development and deployment of Deepfakes detection technology.
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基金项目:国家重点研发计划(2018YFB0804102,2020YFB1804705);浙江省自然科学基金(LR19F020003);浙江省重点研发计划(2019C01055,2020C01021);国家自然科学基金(61772466,U1936215,U1836202);前沿科技创新专项(2019QY(Y)0205) 国家重点研发计划(2018YFB0804102,2020YFB1804705);浙江省自然科学基金(LR19F020003);浙江省重点研发计划(2019C01055,2020C01021);国家自然科学基金(61772466,U1936215,U1836202);前沿科技创新专项(2019QY(Y)0205)
Foundation items:National Key Research and Development Program of China (2018YFB0804102, 2020YFB1804705); Zhejiang Provincial Natural Science Foundation (LR19F020003); Zhejiang Provincial Key Research and Development Program (2019C01055, 2020C01021); National Natural Science Foundation of China (61772466, U1936215, U1836202); Frontier Science and Technology Innovation Project (2019QY(Y)0205)
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LI Xu-Rong,JI Shou-Ling,WU Chun-Ming,LIU Zhen-Guang,DENG Shui-Guang,CHENG Peng,YANG Min,KONG Xiang-Wei.Survey on Deepfakes and Detection Techniques.Journal of Software,2021,32(2):496-518