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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| Published in: | Journal of forensic sciences Vol. 70; no. 3; pp. 1026 - 1043 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.05.2025
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| ISSN: | 0022-1198, 1556-4029, 1556-4029 |
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| Abstract | 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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| AbstractList | 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.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. 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. 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 F 1 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. 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 F 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. 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 F 1 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. |
| Author | Faheem, Muhammad Akram, Arslan Jaffar, Muhammad Arfan Rashid, Javed Boulaaras, Salah Mahmoud |
| AuthorAffiliation | 4 VTT Technical Research Centre of Finland Espoo Finland 3 Department of Mathematics, College of Science Qassim University Buraydah Saudi Arabia 1 Faculty of Computer Science and Information Technology The Superior University Lahore Pakistan 2 Information Technology Services University of Okara Okara Pakistan |
| AuthorAffiliation_xml | – name: 3 Department of Mathematics, College of Science Qassim University Buraydah Saudi Arabia – name: 4 VTT Technical Research Centre of Finland Espoo Finland – name: 2 Information Technology Services University of Okara Okara Pakistan – name: 1 Faculty of Computer Science and Information Technology The Superior University Lahore Pakistan |
| Author_xml | – sequence: 1 givenname: Arslan surname: Akram fullname: Akram, Arslan organization: The Superior University – sequence: 2 givenname: Muhammad Arfan surname: Jaffar fullname: Jaffar, Muhammad Arfan organization: The Superior University – sequence: 3 givenname: Javed orcidid: 0000-0003-3416-9720 surname: Rashid fullname: Rashid, Javed organization: University of Okara – sequence: 4 givenname: Salah Mahmoud surname: Boulaaras fullname: Boulaaras, Salah Mahmoud organization: Qassim University – sequence: 5 givenname: Muhammad surname: Faheem fullname: Faheem, Muhammad email: muhammad.faheem@vtt.fi organization: VTT Technical Research Centre of Finland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40177991$$D View this record in MEDLINE/PubMed |
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| Keywords | digital forensics image authentication image splicing detection feature fusion image forgery localization U‐shaped network |
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| Snippet | 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... 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... |
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| SubjectTerms | 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 |
| Title | CMV2U‐Net: A U‐shaped network with edge‐weighted features for detecting and localizing image splicing |
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