A Multi-View Feature Extraction and Dual-Edge Contrastive Learning Approach for Image Forgery Detection-Implementation Code

Solemnly declare: If you use this open source content in papers, books, academic reports and other works, please quote the following documents (the original link has the latest citation format): XU Zhuang, YE Ziyi, PAN Enkang, LIU Chunxiao. A Multi-View Feature Extraction and Dual-Edge Contrastive Learning Approach for Image Forgery Detection[J]. Journal of Electronics & Information Technology, in press. doi: 10.11999/JEIT251271 Authors: XU Zhuang, YE Ziyi, PAN Enkang, LIU ChunxiaoAuthor：School of Computer Science and Technology, Zhejiang Gongshang UniversityDOI：10.11999/JEIT251271Original：jeit.ac.cn/cn/article/doi/10.11999/JEIT251271Correspondents: LIU Chunxiao，cxliu@mail.zjgsu.edu.cnOpen source date: April 24(th), 2026Funds: National Natural Science Foundation of China (61976188), Zhejiang Provincial Natural Science Foundation of China (LY24F020004), National College Students Innovation and Entrepreneurship Training Program (202510353027), Zhejiang Provincial College Students Innovation and Entrepreneurship Training Program (S202510353076) Open source content1 A Multi-View Feature Extraction and Dual-Edge Contrastive Learning Approach for Image Forgery Detection-Implementation CodeAbstract:   Objective  Image forgery detection technology holds significant application value in fields such as news auditing and judicial forensic authentication.

Aiming at the label conflict problem of existing methods under the definition of pixel-wise classification task, as well as the limitation that tampering cue mining is mostly concentrated in the spatial domain while features from other perspectives are neglected, this paper proposes an improved image forgery detection model based on intra-image inconsistency, and a corresponding image forgery detection algorithm based on multi-view feature extraction and dual-edge contrastive learning.