Computer Science ›› 2020, Vol. 47 ›› Issue (9): 283-292.doi: 10.11896/jsjkx.200400130

• Information Security • Previous Articles     Next Articles

Overview of Deepfake Video Detection Technology

BAO Yu-xuan, LU Tian-liang, DU Yan-hui   

  1. College of Police Information Engineering and Network Security,People’s Public Security University of China,Beijing 100038,China
  • Received:2020-04-28 Published:2020-09-10
  • About author:BAO Yu-xuan,born in 1997,master.His main research interests include cyber security and artificial intelligence.
    LU Tian-liang,born in 1985,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include cyber security and artificial intelligence.
  • Supported by:
    National Key R&D Program of China (20190178) and Fundamental Research Funds for the Central Universities of PPSUC (2020JKF101).

Abstract: 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.

Key words: Deepfake, Deep learning, Feature extraction, Video frame, Multimedia forensics

CLC Number: 

  • TP309
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