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

Particularly in validating image integrity, advances in digital image analysis have profoundly affected forensic investigation. The growing reliance on digital image technology can be attributed in part to the broad availability of consistent and effective image‐capturing technologies. The simplicit...

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Vydáno v:Journal of forensic sciences Ročník 70; číslo 5; s. 1801 - 1823
Hlavní autoři: Akram, Arslan, Jaffar, Muhammad Arfan, Rashid, Javed, Mahmood, Khalid, Ghani, Anwar
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Wiley Subscription Services, Inc 01.09.2025
Témata:
Brightness
Clustering
Compression
copy move localization
Deep learning
Editing
Euclidean geometry
fast Fourier transform
Fast Fourier transformations
Forensic analysis
Forensic science
Forgery
Frame analysis
Fraud
Image analysis
image authentication
Image compression
Image manipulation
Integrity
key points extraction
Localization
Manipulation
Morality
Multimedia
multi‐object detection
Photography
Robustness
Service increment for teaching
SIFT features
Simplicity
Transformation
image authentication
copy move localization
SIFT features
key points extraction
fast Fourier transform
multi‐object detection
ISSN:0022-1198, 1556-4029, 1556-4029
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Shrnutí:Particularly in validating image integrity, advances in digital image analysis have profoundly affected forensic investigation. The growing reliance on digital image technology can be attributed in part to the broad availability of consistent and effective image‐capturing technologies. The simplicity of changing image content thanks to advanced image‐editing technologies presents fresh difficulties for forensic analysis. A structured hybrid framework is presented for finding important objects in images. It does this by using fast Fourier transformation (FFT) for frequency domain filtering, scale‐invariant feature transformation (SIFT), and oriented FAST and rotated BRIEF (ORB) to pull out key points. The MobilenetV2 and VGG16 models extract features from key point areas to detect copy‐move forgery. After that, an attention mechanism combines and normalizes these aspects. Key point matching uses the Euclidean distance; DBSCAN clustering groups pertinent key points for object localization. The suggested approach shows better performance than current methods and detects image copy‐move forgery rather successfully. The framework's robustness is verified against image blurring, contrast alteration, color reduction, image compression, and brightness change among other post‐processing techniques. Since photographs are altered, traditional approaches can struggle with a lot of variety; however, the proposed method combines advanced deep learning models and clustering techniques to make detection more accurate. Extensive testing on five benchmark copy‐move forgeries datasets reveals that the suggested strategy may beat present techniques. This work offers a sophisticated automated approach to guarantee digital image integrity and identify image manipulation.
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ISSN:0022-1198
1556-4029
1556-4029
DOI:10.1111/1556-4029.70076