CMV2U‐Net: A U‐shaped network with edge‐weighted features for detecting and localizing image splicing

The practice of cutting and pasting portions of one image into another, known as “image splicing,” is commonplace in the field of image manipulation. Image splicing detection using deep learning has been a hot research topic for the past few years. However, there are two problems with the way deep l...

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Bibliographic Details
Published in:Journal of forensic sciences Vol. 70; no. 3; pp. 1026 - 1043
Main Authors: Akram, Arslan, Jaffar, Muhammad Arfan, Rashid, Javed, Boulaaras, Salah Mahmoud, Faheem, Muhammad
Format: Journal Article
Language:English
Published: United States Wiley Subscription Services, Inc 01.05.2025
John Wiley and Sons Inc
Subjects:
Agnosticism
Compression
Data loss
Deep learning
digital forensics
Encoding
Extraction
Feature extraction
feature fusion
Forgery
image authentication
Image compression
image forgery localization
Image manipulation
Image processing
image splicing detection
Learning
Localization
Manipulation
Methodological problems
Original Paper
U‐shaped network
digital forensics
image authentication
image splicing detection
feature fusion
image forgery localization
U‐shaped network
ISSN:0022-1198, 1556-4029, 1556-4029
Online Access:Get full text
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Summary:The practice of cutting and pasting portions of one image into another, known as “image splicing,” is commonplace in the field of image manipulation. Image splicing detection using deep learning has been a hot research topic for the past few years. However, there are two problems with the way deep learning is currently implemented: first, it is not good enough for feature fusion, and second, it uses only simple models for feature extraction and encoding, which makes the models vulnerable to overfitting. To tackle these problems, this research proposes CMV2U‐Net, an edge‐weighted U‐shaped network‐based image splicing forgery localization approach. An initial step is the development of a feature extraction module that can process two streams of input images simultaneously, allowing for the simultaneous extraction of semantically connected and semantically agnostic features. One characteristic is that a hierarchical fusion approach has been devised to prevent data loss in shallow features that are either semantically related or semantically irrelevant. This approach implements a channel attention mechanism to monitor manipulation trajectories involving multiple levels. Extensive trials on numerous public datasets prove that CMV2U‐Net provides high AUC and F1 in localizing tampered regions, outperforming state‐of‐the‐art techniques. Noise, Gaussian blur, and JPEG compression are post‐processing threats that CMV2U‐Net has successfully resisted.
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ISSN:0022-1198
1556-4029
1556-4029
DOI:10.1111/1556-4029.70033