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-1.2
Paper Title IMPROVING THE GENERALIZATION ABILITY OF DEEPFAKE DETECTION VIA DISENTANGLED REPRESENTATION LEARNING
Authors Jiashang Hu, Shilin Wang, Xiaoyong Li, Shanghai Jiao Tong University, 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 Deepfake refers to a deep learning based technology which can synthesize visually realistic face images/videos. The misuse of this technology poses a great threat to the society. Although numerous approaches have been proposed to detect Deepfake forgeries, their generalization ability on unseen datasets is limited. In this paper, we propose a new approach that detects human face forgeries by automatically locating the forgery-related region to make the final decision. The proposed network contains two modules, including: the disentanglement module to extract forgery relevant information and the classification module to detect the manipulation artifacts from various regions at different scales. The experiment results on three widely used Deepfake datasets show that the proposed approach can achieve high detection accuracies and outperforms several state-of-the-arts methods especially when evaluated on the unseen datasets.