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

• 论文 • 上一篇    下一篇

基于混合灰度序模式的图像复制-粘贴篡改盲鉴别算法

朱叶1, 2, 申铉京1, 2, 陈海鹏1, 2   

  1. 1.吉林大学 计算机科学与技术学院,长春 130012;
    2.吉林大学 符号计算与知识工程教育部重点实验室,长春 130012
  • 收稿日期:2016-06-15 出版日期:2017-07-20 发布日期:2017-07-20
  • 通讯作者: 陈海鹏(1978-),男,副教授,博士.研究方向:图像处理与模式识别,多媒体信息安全.E-mail:chenhp@jlu.edu.cn
  • 作者简介:朱叶(1989-),女,博士研究生.研究方向:图像盲鉴别.E-mail:zhuye13@mails.jlu.edu.cn
  • 基金资助:
    国家自然科学基金青年基金项目(61305046, 61602203); 吉林省自然科学基金项目(20140101193JC, 20150101055JC).

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

摘要： 针对目前复制-粘贴篡改盲检测算法对光照变换操作鲁棒性较差的问题,提出了一种基于混合灰度序模式(Mixed Intensity Order Pattern, MIOP)的复制-粘贴篡改盲鉴别算法。首先,对待检测图像提取高斯差分区域(Difference of Gaussians, DOG)。其次,利用MIOP特征描述区域。最后,匹配特征并利用RANSAC (RANdom SAmple Consensus)去除误匹配,确定图像的复制-粘贴篡改区域。实验结果表明:本文算法不仅对几何变换和光照操作的检测率较高,且对高斯模糊、噪声和JPEG重压缩等后处理操作鲁棒性较好。

关键词: 计算机应用, 盲鉴别, 图像复制-粘贴篡改, 光照变换, 混合灰度序模式

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

中图分类号: 

  • 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] 刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850.
[2] 王利民,刘洋,孙铭会,李美慧. 基于Markov blanket的无约束型K阶贝叶斯集成分类模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1851-1858.
[3] 金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866.
[4] 赵东,孙明玉,朱金龙,于繁华,刘光洁,陈慧灵. 结合粒子群和单纯形的改进飞蛾优化算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1867-1872.
[5] 刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[6] 欧阳丹彤, 范琪. 子句级别语境感知的开放信息抽取方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1563-1570.
[7] 刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599.
[8] 桂春, 黄旺星. 基于改进的标签传播算法的网络聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1600-1605.
[9] 刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.
[10] 车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.
[11] 赵宏伟, 刘宇琦, 董立岩, 王玉, 刘陪. 智能交通混合动态路径优化算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12] 黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13] 傅文博, 张杰, 陈永乐. 物联网环境下抵抗路由欺骗攻击的网络拓扑发现算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14] 曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15] 侯永宏, 王利伟, 邢家明. 基于HTTP的动态自适应流媒体传输算法[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!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发