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LGDF-Net: Local and Global Feature Based Dual-Branch Fusion Networks for Deepfake Detection

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成果类型:
期刊论文
作者:
Min Long;Zhenyu Liu;Le-Bing Zhang;Fei Peng
作者机构:
[Min Long] School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, Guangdong, China
[Zhenyu Liu] School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
[Fei Peng] School of Artificial Intelligence, Guangzhou University, Guangzhou, Guangdong, China
[Le-Bing Zhang] School of Computer and Artificial Intelligence, Huaihua University, Huaihua, China
语种:
英文
期刊:
IEEE Transactions on Circuits and Systems for Video Technology
ISSN:
1051-8215
年:
2025
页码:
1-1
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62072055, 62302113 and 62372128) 10.13039/501100004735-Natural Science Foundation of Hunan Province (Grant Number: 2022JJ50318) 10.13039/501100003453-Natural Science Foundation of Guangdong Province (Grant Number: 2023A1515011575)
机构署名:
本校为其他机构
院系归属:
计算机与通信工程学院
摘要:
With the rapid development of Deepfake technology, social security is facing great challenges. Although numerous Deepfake detection algorithms based on traditional CNN frameworks perform well on specific datasets, they still suffer from overfitting due to an over-reliance on localized artifact information. This limitation leads to degraded detection performance across diverse datasets. To address this issue, this study proposes a dual-branch fusion network called LGDF-Net. LGDF-Net uses a dual-branch structure to process the local artifact features and global texture features generated by Deep...

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