Region Duplication Detection Using Image Feature Matching
By Xunyu Pan, Siwei Lyu. IEEE Transactions on Information Forensics and Security, 2010
442 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/a885771f37f31dd39bb264a52c4b27b47e4e79a6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luisa Verdoliva
- Davide Cozzolino
- Hany Farid
- Vijay Harishchandra Mankar
- Gajanan K. Birajdar
- Giovanni Poggi
- Eric Kee
- Micah K. Johnson
In Media Technology
- Luisa Verdoliva
- Davide Cozzolino
- Hany Farid
- Giovanni Poggi
- Chi-Man Pun
- Irene Amerini
- Roberto Caldelli
- Xiu-Li Bi
Citations in the corpus
- Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching (2015, methodology): “97%, and it can be easily observed that our proposed scheme performs much better than the keypoint-based forgery detection methods, SIFT [15], [16] and SURF [17], [19].”
- Copy-move forgery detection and localization by means of robust clustering with J-Linkage (2013, methodology): “In the work of Pan and Lyu [25], SIFT features are chosen in order to localize the copied region through the use of a correlation map.”
- Digital image forgery detection using passive techniques: A survey (2013, methodology): “Xunyu and Siwei (2011) developed a region duplication method by estimating the transform between matched SIFT keypoints that is robust to distortions based on image feature matching.”
- Segmentation-Based Image Copy-Move Forgery Detection Scheme (2015, methodology): “1 In some papers like [13]–[15], the authors estimated the transform matrix between the copying source region and pasting target region as well as detecting CMF in the image.”
- Efficient Dense-Field Copy–Move Forgery Detection (2015, methodology): “Keypoint-based methods, working on a relatively small set of pixels, are usually much faster than those based on dense matching.”
- Digital Image Authentication From JPEG Headers (2011, methodology): “Digital forensic techniques have been developed to detect various traces of tampering: region duplication [1]–[4]; resampling [5], [6]; color filter array artifacts [7], [8]; inconsistencies in camera response function [9]; inconsistencies in lighting and shadows [10]–[13]; inconsistencies in…”