Deepfake Video Detection Method Improved by GRU and Involution

被引：1

作者：

Liu, Yalin ^{[1
]}

Lu, Tianliang ^{[1
]}

机构：

[1] College of Information and Cyber Security, People’s Public Security University of China, Beijing

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 22期

关键词：

capsule network; deepfake; focalloss; gated recurrent unit （GRU）; Involution;

D O I：

10.3778/j.issn.1002-8331.2206-0510

中图分类号：

学科分类号：

摘要：

In recent years, the wide spread of deepfake video on the network has caused a negative impact. In order to solve the problems of low accuracy of existing detection models and insufficient and comprehensive information extraction, an improved deepfake video detection method based on gated recurrent unit （GRU）and Involution is proposed. Firstly, a feature extraction network is constructed based on Involution operator to extract global feature information, which enhances the spatial modeling ability of face image from spatial and channel information. Then, the temporal features are extracted through the location and inter-frame information of the main capsule layer and GRU concern features. Finally, focalloss is used as the loss function to balance the samples in the training model phase. The method is tested in Deepfakes, FaceSwap and Celeb-DF datasets, and the experimental results show that the method is better than the mainstream detection methods. The improved comparative experiments further prove the effectiveness of the detection method. © The Author(s) 2023.

引用

页码：276 / 283

页数：7

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