基于边界信息与对比学习的图像篡改检测网络

doi:10.16451/j.cnki.issn1003-6059.202507006

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Abstract To address the insufficient utilization of edge information and high-frequency features in image tampering detection under complex scenarios, an image tampering detection network based on edge information and contrastive learning(EICL-Net) is proposed. First, a dynamic weight update strategy is designed to enhance the feature extraction capability for high-frequency image information. Next, by integrating edge detection algorithms with tampered region detection algorithms, the edge features of images are extracted and enhanced, and the saliency of anomalous information is improved. Finally, a contrastive learning mechanism is introduced to optimize the ability to distinguish pixel distribution differences by constructing positive and negative sample pairs for feature comparison, thereby achieving precise localization of tampered regions. Experiments on multiple public datasets demonstrate that EICL-Net exhibits strong generalization performance and the ability to identify subtle tampering traces under complex scenarios. Therefore, EICL-Net offers a solution to image tampering detection. With its high practical application value, EICL-Net can be widely applied in the fields such as information security and digital forensics.

Key words： Image Tampering Detection Deep Learning Contrastive Learning Loss Function Optimization Boundary Information

Received: 21 April 2025

ZTFLH:

TP391.41

Fund:Supported by 2025 Gansu Provincial Young Scholars Support Program for Universities(No.2025QB-076), Gansu Provincial Innovation Fund for Higher Education Institutions(No.2023B-118), Scientific Research Innovation Project of Gansu University of Political Science and Law(No.GZF2022XZD07), Scienti-fic Research Project of the Judicial Appraisal Center of Gansu University of Political Science and Law(No.jdzxyb2018-09), Project of Gansu Provincial Science and Technology Innovation Platform of Universities(No.2024CXPT-22), Industrial Support Project for Higher Education Institutions of Gansu Province(No.CYZC-2024-24).

Corresponding Authors: WANG Yiqun, Ph.D., associate professor. His research inte- rests include intelligent computing, pattern recognition and machine vision.

About author:: GAO Yancheng, Master student. His research interests include false news detection and com- puter vision.

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WANG Yiqun,GAO Yancheng. Image Tampering Detection Network Based on Edge Information and Contrastive Learning[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(7): 655-667.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202507006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I7/655

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