吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (4): 1280-1285.doi: 10.13229/j.cnki.jdxbgxb201704038

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Copy-move forgery detection based on mixed intensity order pattern

ZHU Ye1, 2, SHEN Xuan-jing1, 2, CHEN Hai-peng1, 2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun 130012, China
  • Received:2016-06-15 Online:2017-07-20 Published:2017-07-20

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Abstract: In order to solve the low robustness of copy-move forgery detection with illumination change, a novel method based on Mixed Intensity Order Pattern (MIOP) was proposed. First, the Difference of Gaussian (DOP) regions of the image to be detected are extracted. Second, the regions are described using MIOP. Third, the features are matched and the false matching is removed by RANdom SAmple Consensus (RANSAC). Finally, the copy-move forgery regions are detected. Experimental results show that the proposed method not only has high accuracy on geometric transformation and illumination change, but also has high robustness on post-processing operations, such as Gaussian blur, noise and JPEG recompression.

Key words: computer application, blind identification, copy-move forgery, illumination change, mixed intensity order pattern

CLC Number: 

  • TP391
[1] Fridrich A J, Soukal B D, Lukáš A J. Detection of copy-move forgery in digital images[C]//Proc of Digital Forensic Research Workshop,2003.
[2] Popescu A C, Farid H. Exposing digital forgeries by detecting duplicated image regions[R]. TR 2004-515,Dartmouth College, 2004.
[3] Bayram S, Sencar H T, Memon N. An efficient and robust method for detecting copy-move forgery[C]//Proc of the 2009 IEEE Int Conf on Acoustics, Speech and Signal Processing. Piscataway,NJ,2009:1053-1056.
[4] Liu Guang-jie, Wang Jun-wen, Lian Shi-guo, et al. A Passive image authentication scheme for detecting region-duplication forgery with rotation[J]. Journal of Network and Computer Applications, 2011, 34(5): 1557-1565.
[5] Mahdian B, Saic S. Detection of copy-move forgery using a method based on blur moment invariants[J]. Forensic Science International, 2007, 171(2): 180-189.
[6] Ryu S J, Kirchner M, Lee M J, et al. Rotation invariant localization of duplicated image regions based on Zernike moments[J]. IEEE Transactions on Information Forensics and Security,2013,8(8):1355-1370.
[7] Cozzolino D,Poggi G,Verdoliva L.Efficient dense-field copy-move forgery detection[J].IEEE Transactions on Information Forensics and Security,2015,10(11):2284-2297.
[8] Amerini I, Ballan L, Caldelli R, et al. A sift-based forensic method for copy-move attack detection and transformation recovery[J]. IEEE Transactions on Information Forensics and Security,2011,6(3):1099-1110.
[9] Bo X, Junwen W, Guangjie L, et al. Image copy-move forgery detection based on SURF[C]//International Conference on Multimedia Information Networking and Security(MINES),2010:889-892.
[10] Guo J M, Liu Y F, Wu Z J. Duplication forgery detection using improved DAISY descriptor[J]. Expert Systems with Applications, 2013, 40(2):707-714.
[11] Zhu Ye, Shen Xuan-jing, Chen Hai-peng. Copy-move forgery detection based on scaled ORB[J]. Multimedia Tools and Applications, 2016, 75(6): 3221-3233.
[12] Wang Zhen-hua, Fan Bin, Wang Gang, et al. Exploring local and overall ordinal information for robust feature description[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2016,38(1):2698-2211.
[13] Lowe G D. Object recognition from local scale-invariant features[J]. Proceeding of the International Conference on Computer Vision (ICCV),1999,2:1150-1157.
[14] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.
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