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local region Frequency Guided Dynamic Inconsistency Network for Deepfake Video Detection

Big Data Mining and Analytics 2024年第3期7卷 889-904页

作者： Pengfei Yue Beijing Chen Zhangjie Fu Engineering Research Center of Digital Forensics affiliated with Ministry of Education School of Computer Science and also with Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology Nanjing 210044China

In recent years,with the rapid development of deepfake technology,a large number of deepfake videos have emerged on the Internet,which poses a huge threat to national politics,social stability,and personal *** many existing deepfake detection methods exhibit excellent performance for known manipulations,their detection capabilities are not strong when faced with unknown ***,in order to obtain better generalization ability,this paper analyzes global and local inter-frame dynamic inconsistencies from the perspective of spatial and frequency domains,and proposes a local region Frequency Guided Dynamic Inconsistency Network(LFGDIN).The network includes two parts:Global SpatioTemporal Network(GSTN)and local region Frequency Guided Module(LRFGM).The GSTN is responsible for capturing the dynamic information of the entire face,while the LRFGM focuses on extracting the frequency dynamic information of the eyes and *** LRFGM guides the GTSN to concentrate on dynamic inconsistency in some significant local regions through local region alignment,so as to improve the model's detection *** on the three public datasets(FF++,DFDC,and Celeb-DF)show that compared with many recent advanced methods,the proposed method achieves better detection results when detecting deepfake videos of unknown manipulation types.

关键词： deepfake video detection dynamic inconsistency local region local region frequency

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