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Learning to localize image forgery using boundary-preserving mask R-CNN.

Debjani Chakraborty¹, Sourav Saha¹, Biswajit Halder²

¹Narula Institute of Technology, Kolkata, India.

Journal of Forensic Sciences

|October 29, 2025

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This study introduces a new Boundary-Preserving Mask R-CNN for digital image forgery detection. The framework accurately localizes manipulated regions, even near boundaries, improving multimedia security.

Area of Science:

Computer Vision
Multimedia Security
Digital Forensics

Background:

Digital image manipulation poses significant challenges to multimedia security.
Existing forgery detection methods often lack precision in localizing manipulated regions, particularly near boundaries, and struggle with generalization across diverse manipulation types.

Purpose of the Study:

To develop an advanced digital image forgery detection framework.
To enhance the accuracy and robustness of manipulated region localization, especially near image boundaries.

Main Methods:

Proposed a novel Boundary-Preserving Mask R-CNN framework.
Integrated channel attention mechanisms for detailed spatial information capture.
Utilized frequency domain features for improved robustness.

Keywords:

ROC boundary‐preserving mask R‐CNN convolutional neural networks (CNNs)deep learning in forensics digital forensics image forgery detection tampered region localization

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Evaluated on six benchmark datasets (CASIA V2, Columbia, Carvalho, CoMoFoD, MICC-F220, CG-1050) covering splicing, copy-move, and compositing manipulations.

Employed extensive preprocessing and pixel-level segmentation for accurate region detection.

Main Results:

The Boundary-Preserving Mask R-CNN demonstrated strong performance across multiple evaluation metrics.
Achieved high accuracy, precision, recall, F1-score, IoU, and AUC.
Showcased superior localization accuracy, particularly for manipulations near image boundaries.
Exhibited robustness across various manipulation types and datasets.

Conclusions:

The proposed framework offers a reliable and precise solution for digital image forgery detection.
It significantly advances the state-of-the-art in localizing manipulated image regions.
The method holds substantial potential for applications in digital forensics and security investigations.