Deepfake detection technique based on improved transformer model

Zhengyi Ma; Qiming Yu; Run Xue; Yan Li; Haibo Liu; Haining Li; Peilong Lu

doi:10.1117/12.2671814

1 March 2023 Deepfake detection technique based on improved transformer model

Zhengyi Ma, Qiming Yu, Run Xue, Yan Li, Haibo Liu, Haining Li, Peilong Lu

Proceedings Volume 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022); 125960U (2023) https://doi.org/10.1117/12.2671814
Event: International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 2022, Changsha, China

Abstract

Deepfake open source technology has lowered the threshold for AI face swapping to a very low level, making it possible to swap faces with one click. The cost of "disinformation" is greatly reduced, so that some deeply faked pictures and videos can be spread on social networks The social network can spread explosively. However, in the defense layer, there are almost no standardized and automated detection tools for deepfake. There is no such tool. Therefore, whether for individuals or platforms, the time window for fighting fake and disinformation is very short, but it is very difficult. In this paper, we use the Transformer model as a base, improve the model and optimize the structure of the model, so that the model can extract the depth features of the video and build a more accurate and efficient deepfake inspection method.

Citation Download Citation

Zhengyi Ma, Qiming Yu, Run Xue, Yan Li, Haibo Liu, Haining Li, and Peilong Lu "Deepfake detection technique based on improved transformer model", Proc. SPIE 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 125960U (1 March 2023); https://doi.org/10.1117/12.2671814

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KEYWORDS

Video

Transformers

Performance modeling

Data modeling

Feature extraction

Visual process modeling

Optical flow

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