Research on face replacement detection based on deep learning

Yue Qi

doi:10.1117/12.3035248

19 July 2024 Research on face replacement detection based on deep learning

Yue Qi

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 1321328 (2024) https://doi.org/10.1117/12.3035248
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

With the development of artificial intelligence technology, the thresh- old of information tampering is reduced, and the quality of counterfeiting is increased. More and more fake information is difficult to distinguish. If such false information is widely disseminated, it will have a particular impact on society. It is essential to note that if face information is tampered with and widely distributed, it could lead to social unrest or damage an individual's reputation, resulting in trust crisis.A face tampering detection method combined with a self-attention mechanism is proposed to solve that traditional residual networks cannot capture the long-range dependencies between video frames and ignore local critical information. The face regions in the video are extracted firstly. Then the spatial features are extracted by the residual network, and the local critical information is learned by the self-attention block. Finally, the long-range dependencies of the spatial features are captured using the gated recurrent unit classification module. Experiments are conducted on the Face-Forensics++. The detection accuracy of the method is improved compared with several baseline algorithms, indicating that the methprocess effectively detect whether the face region in the video tampers.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yue Qi "Research on face replacement detection based on deep learning", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 1321328 (19 July 2024); https://doi.org/10.1117/12.3035248

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