基于高频小波子带马尔可夫特征的图像拼接检测

doi:10.11772/j.issn.1001-9081.2014.05.1477

计算机应用 ›› 2014, Vol. 34 ›› Issue (5): 1477-1481.DOI: 10.11772/j.issn.1001-9081.2014.05.1477

基于高频小波子带马尔可夫特征的图像拼接检测

袁全桥¹,苏波¹,赵旭东²,李生红²

1. 上海交通大学信息安全工程学院，上海 200240
2. 上海交通大学电子信息与电气工程学院，上海 200240

收稿日期:2013-10-17 修回日期:2014-01-09 出版日期:2014-05-01 发布日期:2014-05-30
通讯作者: 苏波
作者简介:袁全桥（1988-），男，河南南阳人，硕士研究生，主要研究方向：数字图像防伪；苏波（1972-），男，上海人，副教授，博士研究生，主要研究方向：信息内容安全、安全管理；赵旭东（1981-），男，江苏徐州人，博士研究生，主要研究方向：数字图像取证、图像处理；李生红（1971-），男，上海人，教授，博士，主要研究方向：信息内容、信号处理、模式识别。
基金资助:
国家自然科学基金资助项目;国家重点研究发展“973”计划;“十二五”国家科技支撑计划

Image splicing detection based on high frequency wavelet Markov features

YUAN Quanqiao¹,SU Bo¹,ZHAO Xudong²,LI Shenghong²

1. School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2013-10-17 Revised:2014-01-09 Online:2014-05-01 Published:2014-05-30
Contact: SU Bo
Supported by:
National Natural Science Foundation

摘要/Abstract

摘要：

拼接是图像篡改过程中最普遍使用的操作，通过检测拼接可以有效鉴别图像是否经过人为修改。针对拼接操作提出了一种盲检测方法：首先对图像进行小波变换，在比较分析不同小波子带对图像拼接检测的作用后，选取高频子带作为图像变换域信息；接着对小波子带进行差分操作，并将系数取整阈值化后作为马尔可夫状态；最后计算状态间的转移概率作为拼接特征，利用支持向量机（SVM）进行分类。在哥伦比亚图像拼接评测彩色库和灰度库上分别进行实验，证实了选取小波高频子带提取拼接特征的有效性。通过与其他特征提取方法对比，所提出特征在两个评测库上都表现出了更好的检测效果，尤其在彩色评测库上取得了94.6%的识别率。

Abstract:

Splicing is the most universal image tampering operation, detection of which is effective for identifying image tamper. A blind splicing detection method was proposed. The method firstly analyzed the effects of different sub-bands on image splicing detection according to features of wavelet transform. High frequency sub-band was verified to be more appropriate for splicing detection both from theory analysis and experiment results. Secondly, the method conducted difference operation, rounded and made threshold to the coefficients as discrete Markov states, and calculated the state transition probabilities as splicing features. Finally, Support Vector Machine (SVM) was used as classifier, and the features were tested on Columbia image splicing detection evaluation datasets. The experimental results show that the proposed method performs better compared with other features and achieves a detection accuracy rate of 94.6% on the color dataset specially.

中图分类号:

TP391.4

袁全桥苏波赵旭东李生红. 基于高频小波子带马尔可夫特征的图像拼接检测[J]. 计算机应用, 2014, 34(5): 1477-1481.

YUAN Quanqiao SU Bo ZHAO Xudong LI Shenghong. Image splicing detection based on high frequency wavelet Markov features[J]. Journal of Computer Applications, 2014, 34(5): 1477-1481.

参考文献

［1］MISHRA M, ADHIKARY F. Digital image tamper detection techniques〖CD*2〗a comprehensive study ［J］. International Journal of Computer Science and Business Informatics, 2013,2(1):73-85.
［2］COX I J, KILIAN J, LEIGHTON F T, et al. Secure spread spectrum watermarking for multimedia ［J］. IEEE Transactions on Image Processing, 1997,6(12):1673-1687.
［3］NIKOLAIDIS N, PITAS I. Copyright protection of images using robust digital signatures ［C］// Proceedings of the 1996 International Conference on Acoustics, Speech, and Signal Processing. Washington, DC: IEEE Computer Society, 1996,4:2168-2171.
［4］BATTISTI F, CARLI M, NERI A. Image forgery detection by means of no-reference quality metrics ［C］// Proceedings of the 2012 SPIE Conference on Media Watermarking, Security, and Forensics. Bellingham: SPIE, 2012: 8303-8317.
［5］DOYODDORJ M, RHEE K. A blind forgery detection scheme using image compatibility metrics ［C］ // Proceedings of the 2013 IEEE International Symposium on Industrial Electronics. Piscataway: IEEE Press, 2013:1-6.
［6］CIPTASARI R W, RHEE K H, SAKURAI K. Image splicing verification based on pixel-based alignment method ［C］// IWDW '12: Proceedings of the 11th International Conference on Digital Forensics and Watermarking. Berlin: Springer-Verlag, 2013:198-212.
［7］SHAO H, YU T, XU M, et al. Image region duplication detection based on circular window expansion and phase correlation ［J］. Forensic Science International, 2012,222(1/2/3):71-82.
［8］SHI Y Q, CHEN C, CHEN W. A natural image model approach to splicing detection ［C］// MM&Sec '07: Proceedings of the 9th Workshop on Multimedia and Security. New York: ACM, 2007:51-62.
［9］Columbia DVMM Research Lab. Columbia image splicing detection evaluation dataset ［EB/OL］. ［2013-05-10］. http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.htm.
［10］HE Z, LU W, SUN W, et al. Digital image splicing detection based on Markov features in DCT and DWT domain ［J］. Pattern Recognition, 2012,45(12):4292-4299.
［11］WU Q, FU Z. Robust image sequence auto-mosaic method ［J］. Journal of Computer Applications, 2012,32(2):499-503.(吴庆双,付仲良.一种鲁棒的序列图像自动拼接方法［J］.计算机应用,2012,32(2):499-503.)
［12］DAUBECHIES I. Ten lectures on wavelets ［M］. Philadelphia: The Society for Industrial and Applied Mathematics, 1992.
［13］HU R, ZHANG T, GAO H. Blind extraction algorithm of spread-spectrum watermark based on discrete wavelet transform and discrete cosine transform domain ［J］. Journal of Computer Applications, 2013,33(1):138-141. (胡然,张天琪,高鸿兴.基于离散小波变换和离散余弦变换域的扩频水印盲提取算法［J］.计算机应用,2013,33(1):138-141.)
［14］CROUSE M S, NOWAK R D, BARANIUK R G. Wavelet-based statistical signal processing using hidden Markov models ［J］. IEEE Transactions on Signal Processing, 1998,46(4):886-902.
［15］FU D, SHI Y Q, SU W. Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition ［C］// IWDW '06: Proceedings of the 5th International Workshop on Digital Watermarking. Berlin: Springer-Verlag, 2006:177-187.
［16］FARID H, LYU S. Higher-order wavelet statistics and their application to digital forensics ［C］// CVPRW '03: Proceedings of the 2003 Conference on Computer Vision and Pattern Recognition Workshop. Piscataway: IEEE Press, 2003:94-101.
［17］Columbia DVMM Research Lab. Columbia uncompressed image splicing detection evaluation dataset ［EB/OL］. ［2013-06-02］. http://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/.
［18］HSU Y F, CHANG S F. Detecting image splicing using geometry invariants and camera characteristics consistency ［C］// Proceedings of the 2006 International Conference on Multimedia and Expo. Piscataway: IEEE Press, 2006:549-552.
［19］CHANG C C, LIN C J. LIBSVM: a library for support vector machines ［J］. ACM Transactions on Intelligent Systems and Technology, 2011,2(3):1-27.

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基于高频小波子带马尔可夫特征的图像拼接检测

Image splicing detection based on high frequency wavelet Markov features

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