DSNI-Net: Forensic Network for Detecting Splicing in Salt-and-Pepper Noisy Images
Image splicing forensics technology is an important way to detect and locate spliced areas in naturally tampered images. However, common noise, especially salt-and-pepper (s&p) noise, can change pixel values to 0 or 255, significantly reducing the accuracy of splicing forensic methods. As...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10904222/ |
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| author | Wuyang Shan Yijie Xie Pengbo Wang Deng Zou Junying Qiu Jun Li |
| author_facet | Wuyang Shan Yijie Xie Pengbo Wang Deng Zou Junying Qiu Jun Li |
| author_sort | Wuyang Shan |
| collection | DOAJ |
| description | Image splicing forensics technology is an important way to detect and locate spliced areas in naturally tampered images. However, common noise, especially salt-and-pepper (s&p) noise, can change pixel values to 0 or 255, significantly reducing the accuracy of splicing forensic methods. As the noise intensity increases, the impact on forensic methods gradually increases. This paper proposes a forensic method to enhance the robustness of the forensic method in scenarios with high-intensity s&p noise called forensic network for detecting splicing in s&p noisy images (DSNI-Net). The image is first denoised using a combination of residual network and median layer (ML) before forensics. The CNN network extracts shallow and deep feature channels in the image, uses ML to remove s&p noise, and reconstructs the image content through the convolution structure to restore image details. Secondly, the feature comparison structure is used to extract tampering traces during the forensic process. This ensures that the final extracted features are uniquely related to the image itself and are used to reveal areas of image tampering. Experimental results on noisy datasets demonstrate the robustness and effectiveness of DSNI-Net under various intensities of s&p noise. Notably, at a noise intensity level of 5%, DSNI-Net improves forensic performance by 37%. Generalization experiments further confirm that DSNI-Net is robust against other types of noise and JPEG compression. |
| format | Article |
| id | doaj-art-b2f6432717994ff6827afea642a2ad79 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b2f6432717994ff6827afea642a2ad792025-08-20T03:01:19ZengIEEEIEEE Access2169-35362025-01-0113383253834110.1109/ACCESS.2025.354583010904222DSNI-Net: Forensic Network for Detecting Splicing in Salt-and-Pepper Noisy ImagesWuyang Shan0https://orcid.org/0000-0002-3689-1728Yijie Xie1https://orcid.org/0009-0002-7373-5772Pengbo Wang2https://orcid.org/0009-0008-7375-8222Deng Zou3https://orcid.org/0009-0006-3639-4146Junying Qiu4https://orcid.org/0009-0005-9909-1620Jun Li5https://orcid.org/0009-0000-8251-191XCollege of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, ChinaCollege of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, ChinaCollege of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, ChinaCollege of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, ChinaCollege of Fashion and Design Arts, Sichuan Normal University, Chengdu, ChinaCollege of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, ChinaImage splicing forensics technology is an important way to detect and locate spliced areas in naturally tampered images. However, common noise, especially salt-and-pepper (s&p) noise, can change pixel values to 0 or 255, significantly reducing the accuracy of splicing forensic methods. As the noise intensity increases, the impact on forensic methods gradually increases. This paper proposes a forensic method to enhance the robustness of the forensic method in scenarios with high-intensity s&p noise called forensic network for detecting splicing in s&p noisy images (DSNI-Net). The image is first denoised using a combination of residual network and median layer (ML) before forensics. The CNN network extracts shallow and deep feature channels in the image, uses ML to remove s&p noise, and reconstructs the image content through the convolution structure to restore image details. Secondly, the feature comparison structure is used to extract tampering traces during the forensic process. This ensures that the final extracted features are uniquely related to the image itself and are used to reveal areas of image tampering. Experimental results on noisy datasets demonstrate the robustness and effectiveness of DSNI-Net under various intensities of s&p noise. Notably, at a noise intensity level of 5%, DSNI-Net improves forensic performance by 37%. Generalization experiments further confirm that DSNI-Net is robust against other types of noise and JPEG compression.https://ieeexplore.ieee.org/document/10904222/Splicing forensicsimage denoisingsalt-and-pepper noiseconvolutional neural network |
| spellingShingle | Wuyang Shan Yijie Xie Pengbo Wang Deng Zou Junying Qiu Jun Li DSNI-Net: Forensic Network for Detecting Splicing in Salt-and-Pepper Noisy Images IEEE Access Splicing forensics image denoising salt-and-pepper noise convolutional neural network |
| title | DSNI-Net: Forensic Network for Detecting Splicing in Salt-and-Pepper Noisy Images |
| title_full | DSNI-Net: Forensic Network for Detecting Splicing in Salt-and-Pepper Noisy Images |
| title_fullStr | DSNI-Net: Forensic Network for Detecting Splicing in Salt-and-Pepper Noisy Images |
| title_full_unstemmed | DSNI-Net: Forensic Network for Detecting Splicing in Salt-and-Pepper Noisy Images |
| title_short | DSNI-Net: Forensic Network for Detecting Splicing in Salt-and-Pepper Noisy Images |
| title_sort | dsni net forensic network for detecting splicing in salt and pepper noisy images |
| topic | Splicing forensics image denoising salt-and-pepper noise convolutional neural network |
| url | https://ieeexplore.ieee.org/document/10904222/ |
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