Computer Science ›› 2021, Vol. 48 ›› Issue (7): 86-92.doi: 10.11896/jsjkx.210200127

Special Issue: Artificial Intelligence Security

• Artificial Intelligence Security • Previous Articles     Next Articles

Deepfake Video Detection Based on 3D Convolutional Neural Networks

XING Hao, LI Ming   

  1. College of Data Science,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
  • Received:2021-02-22 Revised:2021-04-29 Online:2021-07-15 Published:2021-07-02
  • About author:XING Hao,born in 1994,master.His main research interests include compu-ter vision and artificial intelligence.(
    LI Ming,born in 1982,Ph.D,professor,Ph.D supervisor.His main research interests include computer vision and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(11771321) and Shanxi Province Plan Project on Science and Technology of Social Development(201703D321032).

Abstract: In recent years,“Deepfake” has attracted widespread attention.It is difficult for people to distinguish Deepfake videos.However,these forged videos will bring huge potential threats to our society,such as being used to make fake news.Therefore,it is necessary to find a method to identify these synthetic videos.In order to solve the problem,a Deepfake video detection model based on 3D CNNS for deepfake detection is proposed.This model notices the inconsistency of temporal and spatial features in the Deepfake video,and 3D CNNS can effectively capture temporal and spatial features of deepfake video.The experimental results show that models based on 3D CNNS have high accuracy rate,and strong robustness on the Deepfake-detection-challenge dataset and Celeb-DF dataset.The detection accuracy of the proposed model reaches 96.25%,and the AUC value reaches 0.92.This model also solves the problem of poor generalization.By comparing with the existing Deepfake detection models,the proposed model is superior to the existing models in terms of detection accuracy and AUC value,which verifies the effectiveness of the proposed model.

Key words: 3D CNNS, Deepfake detection, Spatial features, Synthetic videos, Temporal features

CLC Number: 

  • TP391.41
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