An enhanced copy-move forgery detection using machine learning based hybrid optimization model
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/s11042-022-11977-2.pdf
Reference28 articles.
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2. Asghar K, Sun X, Rosin PL, Saddique M, Hussain M, Habib Z (2019) Edge–texture feature-based image forgery detection with cross-dataset evaluation. Mach Vision Appl 30(7–8):1243–1262. https://doi.org/10.1007/s00138-019-01048-2
3. Bappy JH, Simons C, Nataraj L, Manjunath BS, Roy-Chowdhury AK (2019) Hybrid LSTM and encoder–decoder architecture for detection of image forgeries. IEEE Trans Image Process 28(7):3286–3300. https://doi.org/10.1109/TIP.2019.2895466
4. Berenji HR, Khedkar P (1992) Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans Neural Netw 3(5):724–740
5. Bilal M, Habib HA, Mehmood Z, Saba T, Rashid M (2019) Single and multiple copy–move forgery detection and localization in digital images based on the sparsely encoded distinctive features and DBSCAN clustering. Arab J Sci Eng 1–18. https://doi.org/10.1007/s13369-019-04238-2
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1. Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images;International Journal of Integrated Engineering;2023-08-28
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