A DeepFake compressed video detection method based on dense dynamic CNN

Xiuqing Mao; Lei Sun; Hongmeng Zhang; Shuai Zhang

doi:10.1117/12.2674838

23 May 2023 A DeepFake compressed video detection method based on dense dynamic CNN

Xiuqing Mao, Lei Sun, Hongmeng Zhang, Shuai Zhang

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 1260409 (2023) https://doi.org/10.1117/12.2674838
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

The emergence of DeepFake poses serious risks to data privacy and social stability. We propose an end-to-end DeepFake video detection method based on a dense dynamic convolutional neural network (CNN) to address the poor performance of DeepFake video detection on complex compression formats and datasets of different forgery methods. In this method, extracted face images are clustered and cleaned by cosine similarity, and face images are expanded through data augmentation to improve data diversity. Dynamic dense blocks are incorporated in a CNN to address optimization difficulties in deep neural networks, and an attention mechanism further improves generalization power. Convolution kernel pruning increases processing speed by effectively reducing the computational needs due to dynamic convolution. Experiments demonstrate that this method has better results on DeepFake video detection across compression rates and datasets compared to other network models.

Citation Download Citation

Xiuqing Mao, Lei Sun, Hongmeng Zhang, and Shuai Zhang "A DeepFake compressed video detection method based on dense dynamic CNN", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 1260409 (23 May 2023); https://doi.org/10.1117/12.2674838

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KEYWORDS

Education and training

Video

Convolution

Data modeling

Video compression

Data analysis

Machine learning

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