摘要
深度伪造的滥用,给国家、社会和个人带来了潜在威胁。首先,介绍了深度伪造的概念和当前发展趋势,分析了基于生成对抗网络的深度伪造视频的生成原理和模型,并介绍了视频数据处理算法及主流的深度伪造数据集;其次,综述了基于视频帧内篡改特征的检测方法,针对深度伪造视频帧内的视觉伪影、面部噪声特征的检测问题,介绍了相关机器学习、深度学习等分类算法、模型;然后,针对深度伪造视频在帧间时空状态不一致的情形,阐述了相关时间序列算法和检测方法;接着,介绍了作为检测补充手段的基于区块链溯源的防篡改公共机制和数字水印、视频指纹等信息安全方法;最后,总结了深度伪造视频检测技术的未来研究方向。
The abuse of deepfake brings potential threats to the country,society and individuals.Firstly,this paper introduces the concept and current trend of deepfake,analyzes the generation principle and models of deepfake videos based on generative adversarial networks,and introduces the video data processing algorithms and the mainstream deepfake datasets.Secondly,this paper summarizes the detection methods based on the tampering features in video frames.Aiming at the detection of visual artifacts and facial noise features in deepfake video frames,the classification algorithms and models related to machine learning and deep learning are introduced.Then,specific to inconsistency of time-space state between deepfake video frames,the relevant time series algorithms and detection methods are introduced.Then,the tamper-proof public mechanism based on blockchain tracing and information security methods such as digital watermark and video fingerprinting are introduced as supplementary detection means.Finally,the future research direction of deepfake video detection technology is summarized.
作者
暴雨轩
芦天亮
杜彦辉
BAO Yu-xuan;LU Tian-liang;DU Yan-hui(College of Police Information Engineering and Network Security,People’s Public Security University of China,Beijing 100038,China)
出处
《计算机科学》
CSCD
北大核心
2020年第9期283-292,共10页
Computer Science
基金
国家重点研发计划(20190178)
中国人民公安大学基本科研业务费重大项目(2020JKF101)。
关键词
深度伪造
深度学习
特征提取
视频帧
多媒体取证
Deepfake
Deep learning
Feature extraction
Video frame
Multimedia forensics
