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

• Orginal Article • Previous Articles     Next Articles

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

RICH HTML

0   

PDF (PC)

198

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.
[1] LIU Fu,ZONG Yu-xuan,KANG Bing,ZHANG Yi-meng,LIN Cai-xia,ZHAO Hong-wei. Dorsal hand vein recognition system based on optimized texture features [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1844-1850.
[2] WANG Li-min,LIU Yang,SUN Ming-hui,LI Mei-hui. Ensemble of unrestricted K-dependence Bayesian classifiers based on Markov blanket [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1851-1858.
[3] JIN Shun-fu,WANG Bao-shuai,HAO Shan-shan,JIA Xiao-guang,HUO Zhan-qiang. Synchronous sleeping based energy saving strategy of reservation virtual machines in cloud data centers and its performance research [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1859-1866.
[4] ZHAO Dong,SUN Ming-yu,ZHU Jin-long,YU Fan-hua,LIU Guang-jie,CHEN Hui-ling. Improved moth-flame optimization method based on combination of particle swarm optimization and simplex method [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1867-1872.
[5] LIU En-ze,WU Wen-fu. Agricultural surface multiple feature decision fusion disease judgment algorithm based on machine vision [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1873-1878.
[6] OUYANG Dan-tong, FAN Qi. Clause-level context-aware open information extraction [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1563-1570.
[7] LIU Fu, LAN Xu-teng, HOU Tao, KANG Bing, LIU Yun, LIN Cai-xia. Metagenomic clustering method based on k-mer frequency optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1593-1599.
[8] GUI Chun, HUANG Wang-xing. Network clustering method based on improved label propagation algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1600-1605.
[9] LIU Yuan-ning, LIU Shuai, ZHU Xiao-dong, CHEN Yi-hao, ZHENG Shao-ge, SHEN Chun-zhuang. LOG operator and adaptive optimization Gabor filtering for iris recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1606-1613.
[10] CHE Xiang-jiu, WANG Li, GUO Xiao-xin. Improved boundary detection based on multi-scale cues fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1621-1628.
[11] ZHAO Hong-wei, LIU Yu-qi, DONG Li-yan, WANG Yu, LIU Pei. Dynamic route optimization algorithm based on hybrid in ITS [J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12] HUANG Hui, FENG Xi-an, WEI Yan, XU Chi, CHEN Hui-ling. An intelligent system based on enhanced kernel extreme learning machine for choosing the second major [J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13] FU Wen-bo, ZHANG Jie, CHEN Yong-le. Network topology discovery algorithm against routing spoofing attack in Internet of things [J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14] CAO Jie, SU Zhe, LI Xiao-xu. Image annotation method based on Corr-LDA model [J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15] HOU Yong-hong, WANG Li-wei, XING Jia-ming. HTTP-based dynamic adaptive streaming video transmission algorithm [J]. 吉林大学学报(工学版), 2018, 48(4): 1244-1253.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd