吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (2): 446-453.doi: 10.13229/j.cnki.jdxbgxb201402027

• 论文 • 上一篇    下一篇

基于VAM和E-HMM的图像拼接盲鉴别算法

申铉京1,2, 李响1,2, 吕颖达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:2012-12-27 出版日期:2014-02-01 发布日期:2014-02-01
  • 通讯作者: 陈海鹏（1978- ），男，副教授.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 E-mail:chenhp@jlu.edu.cn
  • 作者简介:申铉京（1958- ），男，教授，博士生导师.研究方向：图像处理与模式识别，多媒体信息安全，智能控制技术.E-mail：xjshen@jlu.edu.cn
  • 基金资助:

    国家青年科学基金项目（61305046）；吉林省青年科学基金项目（20130522117JH）.

Blind detection of image splicing based on visual attention model and extended hidden Markov model

SHEN Xuan-jing1,2, LI Xiang1,2, LYU Ying-da1,2, CHEN Hai-peng1,2   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
  • Received:2012-12-27 Online:2014-02-01 Published:2014-02-01

RICH HTML

0   

PDF (PC)

695

摘要：

为了提高拼接篡改图像的检测准确率，利用视觉注意模型提出了一种新的图像拼接篡改盲鉴别算法。首先，采用改进的基于OSF的非线性滤波方法提取图像的边缘信息，得到边缘显著图像ECM；其次，利用视觉注意模型提取ECM的注意点，并采用显著边缘定位法锁定图像显著边缘处注意点，进而获取图像关键特征片段；接着，提取图像片段的Cr通道，并计算其小波重构图像；然后，针对小波重构图像，提取其扩展的DCT域的HMM特征，并采用SVM-RFE算法对所提取特征进行降维处理；最后，根据得到的特征向量，利用SVM对特征值进行训练并建立分类模型，从而实现自然图像和拼接篡改图像的分类识别。实验结果表明，针对哥伦比亚大学拼接篡改图像库，本文算法的正确检测率为96.32%。

关键词: 计算机应用, 盲鉴别, 图像拼接, 视觉注意模型, 扩展的隐马尔可夫模型

Abstract:

In order to improve the detection accuracy of spliced images, a new blind detection based on the Visual Attention Model (VAM) was proposed in this study. First, the Edge Conspicuous Map (ECM) is created by an improved Order Statistics Filter (OSF) based nonlinear filtering approach; then, the ECM fixations are extracted by VAM, and the fixations on the boundaries are located by conspicuous edge positioning method, accordingly the key feature fragments are captured. Second, the Extended Hidden Markov Model (E-HMM) features are extracted from each wavelet reconstructed image of Cr channel of the fragments, and their dimensions are reduced by SVM-RFE. Finally, the above features are trained and classified using SVM, by which the spliced images can be identified from the natural ones. The experimental results show that, when testing on the Columbia image splicing detection dataset, the detection accuracy of the proposed method is 96.32%.

Key words: computer application, blind identification, image splicing, visual attention model, extended hidden Markov model

中图分类号: 

  • TP391

[1] 吕颖达, 申铉京, 苗健, 等.基于光源方向不一致性的局部光源图像盲鉴别方法[J].吉林大学学报:工学版, 2010, 40(6):1673-1677. LV Ying-da, Shen Xuan-jing, Miao Jian, et al.Blind identification for light source images based on inconsistency in light source direction[J].Journal of Jilin University(Engineering and Technology Edition), 2010, 40(6):1673-1677.

[2] 吴琼, 李国辉, 涂丹, 等. 面向真实性鉴别的数字图像盲取证技术综述[J].自动化学报, 2008, 34(12): 1458-1466. Wu Qiong, Li Guo-hui, Tu Dan, et al. A survey of blind digital image forensics technology for authenticity detection[J]. Acta Automatica Sinica, 2008, 34(12): 1458-1466.

[3] Qu Z, Qiu G, Huang J. Detect digital image splicing with visual cues[C]//Information Hiding.Berlin:Springer Berlin Heidelberg, 2009.

[4] Ng T T, Chang S F, Sun Q. A data set of authentic and spliced image blocks[R]. Columbia University, ADVENT Technical Report, 2004: 203-204.

[5] Lin Z, He J, Tang X, et al. Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis[J]. Pattern Recognition, 2009, 42(11): 2492-2501.

[6] Zheng Q, Sun W, Lu W. Digital spliced image forensics based on edge blur measurement[C]// 2010 IEEE International Conference on Information Theory and Information Security, Beijing, China, 2010.

[7] Dong J, Wang W, Tan T, et al. Run-length and Edge Statistics Based Approach for Image Splicing detection[M]. Berlin:Springer Berlin Heidelberg, 2009.

[8] He Z, Sun W, Lu W, et al. Digital image splicing detection based on approximate run length[J]. Pattern Recognition Letters, 2011, 32(12): 1591-1597.

[9] Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1998, 20(11): 1254-1259.

[10] Shi Y Q, Chen C, Chen W. A natural image model approach to splicing detection[C]//Proceedings of the 9th Workshop on Multimedia & Security, ACM, 2007.

[11] Zhao X, Li J, Li S, et al. Detecting Digital Image Splicing in Chroma Spaces[M]. Berlin:Springer Berlin Heidelberg, 2011.

[12] Guyon I, Weston J, Barnhill S, et al. Gene selection for cancer classification using support vector machines[J]. Machine Learning, 2002, 46(1-3): 389-422.

[13] Chang C C, Lin C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): 27.

[14] Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification[R].Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 2003.
[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