DDformer: Deepfake Detection with Multimodal Fusion Transformer

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成果类型：

会议论文

作者：

Jiazhan Gao;Deqi Huang;Jinlai Zhang;Eksan Firkat;Chao Liu;...

作者机构：

[Jiazhan Gao; Eksan Firkat; Chao Liu] School of Information Science and Engineering, Xinjiang University, 830017, Urumqi, China

[Jinlai Zhang] College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, 410114, Changsha, China

[Deqi Huang] School of Electrical Engineering, Xinjiang University, 830017, Urumqi, China

Department of Precision Instrument, Tsinghua University, 100084, Beijing, China

[Jihong Zhu] School of Information Science and Engineering, Xinjiang University, 830017, Urumqi, China<&wdkj&>Department of Precision Instrument, Tsinghua University, 100084, Beijing, China

语种：

英文

年：

2025

页码：

362-373

会议名称：

Advanced Intelligent Computing Technology and Applications: 21st International Conference, ICIC 2025, Ningbo, China, July 26–29, 2025, Proceedings, Part XXII

出版地：

Berlin, Heidelberg

出版者：

Springer-Verlag

ISBN：

978-981-95-0008-6

DOI：

10.1007/978-981-95-0009-3_31

机构署名：

本校为其他机构

院系归属：

汽车与机械工程学院

摘要：

Early deepfakes primarily focused on visual face swapping, but the advancement of multimodal deepfake technology now allows for realistic face and audio replacements. Although some researchers have made advances in using multimodal learning for deepfake detection, they still encounter two major challenges: heterogeneity and complementary data fusion. We propose a novel approach called DDformer, and introduce two fusion methods: Multimodal Fusion Transformer (MFT) and Shared Weight Attention Fusion (SWAF). MFT utilizes the powerful global modeling capability of the transformer, which enhances t...

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DDformer: Deepfake Detection with Multimodal Fusion Transformer

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