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GLFAFormer: DeepFake forgery detection with adaptive feature extract and align

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成果类型:
期刊论文
作者:
Long Chen;Huihuang Zhao*;Jiaxin He;Weiliang Meng
通讯作者:
Huihuang Zhao
作者机构:
National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, China
[Long Chen; Jiaxin He] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China
Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
[Weiliang Meng] School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
[Huihuang Zhao] National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, China<&wdkj&>College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
通讯机构:
[Huihuang Zhao] N
National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, China<&wdkj&>College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
语种:
英文
期刊:
Digital Signal Processing
ISSN:
1051-2004
年:
2025
页码:
105321
机构署名:
本校为通讯机构
院系归属:
计算机科学与技术学院
摘要:
With the rapid development of generative models, there is an increasing demand for universal fake image detectors. In this paper, we investigate the problem of fake image detection for the synthesis of generative models to detect fake images from multiple generative methods. Recent research methods explore the benefits of pre-trained models and mainly adopt a fixed paradigm of training additional classifiers separately, but we find that the fixed paradigm hinders the full learning of forgery features, leading to insufficient representation learning in the detector.The main reason is that the f...

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