MMFD-Net: A Novel Network for Image Forgery Detection and Localization via Multi-Stream Edge Feature Learning and Multi-Dimensional Information Fusion

Haichang Yin, KinTak U (), Jing Wang () and Zhuofan Gan
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Haichang Yin: Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China
KinTak U: Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China
Jing Wang: CEPREI Certification Body, Guangzhou CEPREI Certification Center Service Co., Ltd., Guangzhou 511370, China
Zhuofan Gan: School of Computer Science, Guangdong University of Finance, Guangzhou 510520, China

Mathematics, 2025, vol. 13, issue 19, 1-24

Abstract: With the rapid advancement of image processing techniques, digital image forgery detection has emerged as a critical research area in information forensics. This paper proposes a novel deep learning model based on Multi-view Multi-dimensional Forgery Detection Networks (MMFD-Net), designed to simultaneously determine whether an image has been tampered with and precisely localize the forged regions. By integrating a Multi-stream Edge Feature Learning module with a Multi-dimensional Information Fusion module, MMFD-Net employs joint supervised learning to extract semantics-agnostic forgery features, thereby enhancing both detection performance and model generalization. Extensive experiments demonstrate that MMFD-Net achieves state-of-the-art results on multiple public datasets, excelling in both pixel-level localization and image-level classification tasks, while maintaining robust performance in complex scenarios.

Keywords: digital image forgery detection; multi-branch multi-dimensional forgery detection; multi-stream edge feature learning module; multi-dimensional information fusion module (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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