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Image forgery detection and localization using deep learning techniques.

Shivam Sharma¹, Devshri Satyarthi², Santosh Singh Rathore²

¹Department of Information Technology, ABV-IIITM Gwalior, Gwalior, India.

Journal of Forensic Sciences

|April 29, 2026

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This study introduces a deep learning framework for automatic image forgery detection and localization. The method accurately identifies tampered images and pinpoints manipulated areas, enhancing digital image forensics.

Area of Science:

Computer Vision
Digital Forensics
Artificial Intelligence

Background:

Digital image manipulation is increasingly sophisticated, challenging authenticity verification.
Reliability concerns exist for digital images in security, forensic, and judicial contexts.
Existing methods struggle with accurate detection and localization of forgeries.

Purpose of the Study:

To propose a unified deep learning framework for automatic image forgery detection and localization.
To enhance the reliability of digital images in critical applications.
To provide a robust solution for identifying tampered visual content.

Main Methods:

A deep learning framework combining Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) for detection.

Keywords:

convolutional neural network deep learning image forgery detection image forgery localization image segmentation

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Utilizing VGG16 and U-Net architectures for pixel-level localization of forged regions.

Experimental validation on COMOFOD v2.0 and Columbia Image Splicing datasets.

Main Results:

The proposed framework achieves state-of-the-art performance in both forgery detection and localization.
Demonstrated superior accuracy, loss, and AUC scores compared to existing methods.
Successfully identified and localized manipulated regions with high precision.

Conclusions:

The developed framework offers an effective and reliable solution for digital image forgery analysis.
Significantly strengthens digital image forensic capabilities.
Improves the trustworthiness of visual content in security-sensitive applications.