Frequency domain deepfake detection based on two-stream neural network

Yijia Xu; Dong Dong Zhang; Chengyu Sun

doi:10.1117/12.2680225

27 June 2023 Frequency domain deepfake detection based on two-stream neural network

Yijia Xu, Dong Dong Zhang, Chengyu Sun

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127052R (2023) https://doi.org/10.1117/12.2680225
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Benefiting from the progress of deep learning driven generative models, face forgery technologies have rapidly become mature, thus raising public concerns about the illegal usage of these technologies. Despite the fact that fake images and videos are often unrecognizable to human eyes, recent work has found that hidden artifacts can be exposed in frequency domain. Our meth-od introduces the idea of separating human face area using landmarks before mining the forgery patterns in frequency domain. This we believe can help the network learn more discriminative features, also, a two-stream learning framework combining single-frame pathway and multi-frame pathway is developed to mine frequency clues. Compared with previous methods, our approach showed good results while using a few training data and little time. The effectiveness of our method is shown on different versions of the FF++ and Celeb-DF dataset.

Citation Download Citation

Yijia Xu, Dong Dong Zhang, and Chengyu Sun "Frequency domain deepfake detection based on two-stream neural network", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127052R (27 June 2023); https://doi.org/10.1117/12.2680225

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