A novel deep learning framework for copy-moveforgery detection in images-Reference-Cited by-同舟云学术

A novel deep learning framework for copy-moveforgery detection in images

Published:2020-03-18 Issue:27-28 Volume:79 Page:19167-19192
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Elaskily Mohamed A.^ORCID,Elnemr Heba A.,Sedik Ahmed,Dessouky Mohamed M.,El Banby Ghada M.,Elshakankiry Osama A.,Khalaf Ashraf A. M.,Aslan Heba K.,Faragallah Osama S.,Abd El-Samie Fathi E.

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/s11042-020-08751-7.pdf

Reference55 articles.

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2. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A SIFT-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans Inf For Secur 6(3)

3. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10:226–245

4. Boz A, Bilge HŞ (2016) Copy-move image forgery detection based on LBP and DCT. 24th Signal Processing and Communication Application Conference (SIU), 16–19

5. Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf For Secur 7(6):1841–1854

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