Fuzzy methaheuristic model for copy-move forgery detection on images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17053-7.pdf
Reference21 articles.
1. Wu C, Zhang X (2020) Total bregman divergence-based fuzzy local information c-means clustering for robust image segmentation. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020.106468
2. Ralevic NM, Karaklic D, Pistinjat N (2019) Fuzzy metric and its applications in removing the image noise. Soft Comput 23(22):12049–12061. https://doi.org/10.1007/s00500-019-03762-5
3. Peralta D, Saeys Y (2020) Robust unsupervised dimensionality reduction based on feature clustering for single-cell imaging data. Appl Soft Comput 106421
4. Wang JWG, Liu J, Zhang A, Dai YW, Wang ZQ (2009) Fast and robust forensics or image region-duplication forgery. Acta Automat Sin 35(12):1488–1495
5. Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy–rotate–move forgery using zernike moments. In: Information hiding. Springer–Verlag. Berlin, pp 51–65
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