Journal of Frontiers of Computer Science and Technology ›› 2023, Vol. 17 ›› Issue (1): 1-26.DOI: 10.3778/j.issn.1673-9418.2205035

• Frontiers·Surveys • Previous Articles     Next Articles

Overview of Facial Deepfake Video Detection Methods

ZHANG Lu, LU Tianliang, DU Yanhui   

  1. 1. Institute of Information and Network Security, People??s Public Security University of China, Beijing 100038, China
    2. Department of Investigation, Shandong Police College, Jinan 250200, China
  • Online:2023-01-01 Published:2023-01-01



  1. 1. 中国人民公安大学 信息网络安全学院,北京 100038
    2. 山东警察学院 侦查系,济南 250200

Abstract: The illegal use of deepfake technology will have a serious impact on social stability, personal reputation and even national security. Therefore, it is imperative to develop research on facial deepfake videos detection tech-nology, which is also a research hotspot in the field of computer vision in recent years. At present, the research is based on traditional face recognition and image classification technology, building a deep neural network to deter-mine a facial video is real or not, but there are still problems such as the low quality of dataset, the combine of multimodal features and the poor performance of model generalization. In order to further promote the development of deepfake video detection technology, a comprehensive summary of various current algorithms is carried out, and the existing algorithms are classified, analyzed and compared. Firstly, this paper mainly introduces the facial deepfake videos detection datasets. Secondly, taking feature selection as the starting point, this paper summarizes the main method of detecting deepfake videos in the past three years, classifies various detection technologies from the pers-pectives of spatial features, spatial-temporal fusion features and biological features, and introduces some new detec-tion methods based on watermarking and blockchain. Then, this paper introduces the new trends of facial deepfake video detection methods from the aspects of feature selection, transfer learning, model architecture and training ideas. Finally, the full text is summarized and the future technology development is prospected.

Key words: deep learning, multimedia forensics, deepfake, video detection, face forgery

摘要: 深度伪造(deepfake)技术的非法应用会对社会稳定、个人名誉甚至国家安全造成恶劣影响,因此针对人脸视频的深度伪造检测成为计算机视觉领域中的难点与研究热点。目前该领域的研究建立在传统人脸识别与图像分类技术基础上,通过搭建深度学习网络判别真伪,但存在数据集质量不一、多模态特征如何有效结合、模型泛化能力较差等问题。为进一步促进深度伪造检测技术的发展,对当前各类人脸视频深度伪造算法进行了全面总结,并对已有算法进行了归类、分析、比较。首先,主要介绍人脸视频深度伪造检测数据集;其次,对近三年主要的伪造视频检测方法进行总结,以特征选择为切入点,从空间特征、时空融合特征、生物特征的角度对各项检测技术进行分类整理,并对基于水印与区块链等非主流检测方法进行介绍;然后,从特征选择、迁移学习、模型设计与训练思路等方面介绍了各类检测方法所呈现出的主流趋势;最后,对全文进行总结并对未来技术发展进行展望。

关键词: 深度学习, 多媒体取证, 深度伪造, 视频检测, 人脸篡改