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Advanced digital image forensics: A hybrid framework for copy-move forgery detection in multimedia security.

Arslan Akram¹, Muhammad Arfan Jaffar¹, Javed Rashid²

¹Faculty of Computer Science and Information Technology, The Superior University, Lahore, Pakistan.

Journal of Forensic Sciences

|May 13, 2025

Summary

This study introduces a hybrid framework for digital image forensics, enhancing copy-move forgery detection. The method combines deep learning with clustering for robust image integrity validation against various manipulations.

Keywords:

SIFT features copy move localization fast Fourier transform image authentication key points extraction multi‐object detection

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Digital Image Forensics
Computer Vision
Artificial Intelligence

Background:

Digital image analysis is crucial for forensic investigations, but advanced editing tools create challenges for verifying image integrity.
Reliance on digital imaging necessitates robust methods to detect sophisticated image manipulations like copy-move forgery.

Purpose of the Study:

To develop a structured hybrid framework for accurate detection of copy-move image forgery.
To enhance the reliability of digital image forensics by addressing limitations of traditional methods.

Main Methods:

Utilized Fast Fourier Transformation (FFT) for frequency domain filtering and Scale-Invariant Feature Transformation (SIFT) and Oriented FAST and Rotated BRIEF (ORB) for key point extraction.
Employed MobilenetV2 and VGG16 models for feature extraction in key point areas, combined with an attention mechanism.
Applied Euclidean distance for key point matching and DBSCAN clustering for object localization to detect copy-move forgeries.

Main Results:

The proposed hybrid framework demonstrated superior performance compared to existing methods in detecting copy-move forgeries.
The framework proved robust against various post-processing techniques, including blurring, contrast alteration, compression, and brightness changes.
Extensive testing on benchmark datasets confirmed the strategy's effectiveness in outperforming current techniques.

Conclusions:

The developed approach offers a sophisticated, automated solution for guaranteeing digital image integrity.
The combination of deep learning models and clustering techniques provides a more accurate detection of image manipulation.
This work contributes a reliable method for forensic analysis in the era of advanced digital image editing.