2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-MMSDF-1.4
Paper Title EXPLOITING FACIAL SYMMETRY TO EXPOSE DEEPFAKES
Authors Gen Li, Yun Cao, Xianfeng Zhao, Institute of Information Engineering, Chinese Academy of Sciences, China
SessionSS-MMSDF-1: Special Session: AI for Multimedia Security and Deepfake 1
LocationArea B
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Special Sessions: Artificial Intelligence for Multimedia Security and Deepfake
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we introduce a new approach to detect synthetic portrait images and videos. Motivated by the observation that the symmetry of synthetic facial area would be easily broken, this approach aims to reveal the tampering trace by features learned from symmetrical facial regions. To do so, a two-stream learning framework is designed which uses a hard sharing Deep Residual Networks as the backbone network. The feature extractor maps the pair of symmetrical face patches to an angular distance indicating the difference of symmetry features. Extensive experiments are carried out to test the effectiveness in detecting synthetic portrait images and videos, and corresponding results show that our approach is effective even on heterogeneous data and re-compression data that were not used to train the detection model.