A new keypoint-based copy-move forgery detection for small smooth regions-Reference-Cited by-同舟云学术

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A new keypoint-based copy-move forgery detection for small smooth regions

Published:2016-11-18 Issue:22 Volume:76 Page:23353-23382
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Wang Xiang-Yang,Li Shuo,Liu Yu-Nan,Niu Ying,Yang Hong-Ying,Zhou Zhi-li

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/article/10.1007/s11042-016-4140-5/fulltext.html

Reference42 articles.

1. Al-Qershi OM, BE Khoo (2015) Enhanced matching method for copy-move forgery detection by means of Zernike moments. 13th International Workshop on Digital-Forensics and Watermarking (IWDW 2014), LNCS 9023, pp 485–497

2. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Process Image Commun 28(6):659–669

3. Amerini I, Ballan L, Caldelli R (2011) A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6(3):1099–1110

4. Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094

5. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded up robust features (SURF). Comput Vis Image Underst 110(3):346–359

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1. A Similarity-Based Positional Attention-Aided Deep Learning Model for Copy–Move Forgery Detection;IEEE Transactions on Artificial Intelligence;2024-09

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