Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix
Author:
Funder
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10216-7.pdf
Reference41 articles.
1. Agarwal, S., & Chand, S. (2015). Image forgery detection using multi scale entropy filter and local phase quantization. Graphical Signal Process, 10, 78–85. https://doi.org/10.5815/ijigsp.2015.10.08
2. Alahmadi, A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G., & Mathkour, H. (2017). Passive detection of image forgery using DCT and local binary pattern. Signal Image and Video Processing, 11(1), 81–88. https://doi.org/10.1007/s11760-016-0899-0
3. Bahrami, K., Kot, A. C., Li, L., & Li, H. (2015). Blurred image splicing localization by exposing blur type inconsistency. IEEE Transactions on Information Forensics and Security, 10(5), 999–1009. https://doi.org/10.1109/TIFS.2015.2394231
4. Bianchi, T., & Piva, A. (2012). Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Transactions on Information Forensics and Security, 7(3), 1003–1017. https://doi.org/10.1109/TIFS.2012.2187516
5. Cozzolino, D., Poggi, G., & Verdoliva, L. (2015). Efficient dense-field copy-move forgery detection. IEEE Transactions on Information Forensics and Security, 10(11), 2284–2297. https://doi.org/10.1109/TIFS.2015.2455334
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