Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 12-30.doi: 10.11896/jsjkx.210900146

• Computer Vision: Theory and Application • Previous Articles     Next Articles

Survey on Generalization Methods of Face Forgery Detection

DONG Lin1, HUANG Li-qing1,2,3, YE Feng1,2,3, HUANG Tian-qiang1,2,3, WENG Bin1,2,3, XU Chao1,2,3   

  1. 1 College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117,China
    2 Digital Fujian Institute of Big Data Security Technology,Fuzhou 350117,China
    3 Fujian Provincial Engineering Research Center of Big Data Analysis and Application,Fuzhou 350117,China
  • Received:2021-09-16 Revised:2021-10-25 Online:2022-02-15 Published:2022-02-23
  • About author:DONG Lin,born in 1999,postgraduate,is a member of China Computer Federation.Her main research interests include computer vision and multi-media security.
    YE Feng,born in 1978,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include computer vision and artificial intelligence.
  • Supported by:
    National Key R&D Program Special Fund(2018YFC1505805),National Natural Science Foundation of China(62072106),Science and Technology Plan Innovation Strategy Research of Fujian Province,China(2020R0178,2021R0041) and Education Department Program of Fujian Province,China(JT180078).

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Abstract: The rapid development of deep learning technology provides powerful tools for the research of deepfake.Forged videos and images are more and more difficult for human eyes to distinguish between real and fake.Videos and images on the internet may have a huge negative impact on social life,such as financial fraud,the spread of fake news,and personal bullying.At present,the fake face detection technology based on deep learning has reached a high accuracy on multiple benchmark databases such as FaceForensics++,but the detection accuracy on cross-databases is much lower than accuracy on the source database,that is,it is difficult for many detection methods to generalize to different types of forgeries,or unknown types of forgeries,which also motivates more scholars to focus on generalization methods.The generalization research of face forgery detection focuses on methods based on deep learning.Firstly,the commonly used datasets including real-world datasets and multi-task datasets for forgery detection are discussed and compared.Secondly,it classifies and summarizes the generalization of video and image tampering detection from three aspects:data,features,and learning strategies.The data refers to data augmentation in deepfake detection.The features include single-domain features such as frequency domain features and multi-domain features.The learning strategies consist of transfer learning,multi-task learning,meta-learning,and incremental learning.And the advantages and shortcomings of three different types are analyzed.Finally,the future development direction and challenges of face tampering detection generalization are discussed.

Key words: Face forgery detection, Generalization, Media forensics, Video image classification, Video image tampering

CLC Number: 

  • TP309
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