Copy-move forgery detection based on multifractals
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
http://link.springer.com/content/pdf/10.1007/s11042-019-7277-1.pdf
Reference40 articles.
1. Alahmadi A, Hussain M, Aboalsamh H, Muhammad G, Bebis G, Mathkour H (2017) Passive detection of image forgery using DCT and local binary pattern. SIViP 11(1):81–88. https://doi.org/10.1007/s11760-016-0899-0
2. Alkawaz MH, Sulong G, Saba T, Rehman A (2016) Detection of copy-move image forgery based on discrete cosine transform. Neural Comput & Applic:1–10. https://doi.org/10.1007/s00521-016-2663-3
3. Bi X, Pun CM (2018) Fast copy-move forgery detection using local bidirectional coherency error refinement. Pattern Recogn 81:161–175. https://doi.org/10.1016/j.patcog.2018.03.028
4. Bi X, Pun CM, Yuan XC (2016) Multi-level dense descriptor and hierarchical feature matching for copy–move forgery detection. Inf Sci 345:226–242. https://doi.org/10.1016/j.ins.2016.01.061
5. Bi X, Pun CM, Yuan XC (2018) Multi-scale feature extraction and adaptive matching for copy-move forgery detection. Multimed Tools Appl 77(1):363–385. https://doi.org/10.1007/s11042-016-4276-3
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