联合压缩感知和颜色向量角的彩色图像哈希方法

doi:10.3969/j.issn.0255-8297.2015.06.003

应用科学学报 ›› 2015, Vol. 33 ›› Issue (6): 595-603.doi: 10.3969/j.issn.0255-8297.2015.06.003

联合压缩感知和颜色向量角的彩色图像哈希方法

刘凯^1,2,3, 唐振军^1,2, 张显全^1,2, 俞春强¹

1. 广西师范大学广西多源信息挖掘与安全重点实验室, 广西桂林 541004;
2. 广西师范大学计算机科学与信息工程学院, 广西桂林 541004;
3. 湖南农业大学理学院, 长沙 410128

收稿日期:2015-04-23 修回日期:2015-06-10 出版日期:2015-11-30 发布日期:2015-11-30
通信作者: 唐振军,教授,研究方向:图像处理与多媒体信息安全,E-mail:tangzj230@163.com,zjtang@gxnu.edu.cn E-mail:tangzj230@163.com,zjtang@gxnu.edu.cn
基金资助:
国家自然科学基金(No.61562007, No.61300109, No.61363034);广西自然科学基金(No.2012GXNSFBA053166);广西高等学校科研项目基金(No.YB2014048);广西多源信息挖掘与安全重点实验室系统性研究基金(No.14-A-02-02, No.13-A-03-01);广西高等学校优秀中青年骨干教师培养工程项目基金(No.GXQG012013059)资助

Color Image Hashing with Compressive Sensing and Color Vector Angle

LIU Kai^1,2,3, TANG Zhen-jun^1,2, ZHANG Xian-quan^1,2, YU Chun-qiang¹

1. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin 541004, Guangxi Province, China;
2. College of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, Guangxi Province, China;
3. College of Science, Hunan Agriculture University, Changsha 410128, China

Received:2015-04-23 Revised:2015-06-10 Online:2015-11-30 Published:2015-11-30

摘要/Abstract

摘要： 提出一种联合压缩感知和颜色向量角的彩色图像哈希方法.该方法先对输入图像进行预处理,并计算其颜色向量角矩阵,然后对矩阵进行非重叠分块,再将每一块进行压缩感知测量,用测量向量的均值构成哈希值.实验表明,该方法对常见数字操作稳健并有良好的唯一性,分类性能优于3种现有方法.

关键词: 图像哈希, 颜色向量角, 压缩感知, 测量向量

Abstract: This paper presents a hashing method for color images based on compressive sensing and color vector angles. In the preprocessing the input image is first normalized. The normalized image is then converted to a color vector angle matrix, which is further divided into non-overlapping blocks. Compressive sensing is applied to each block, and the mean of measurement vector is used to form the image hash. Experiments show that the proposed method is robust against normal digital operations, has good discrimination capability, and outperforms three existing methods.

Key words: image hash, color vector angle, measurement vector, compressive sensing

中图分类号:

TN391

刘凯, 唐振军, 张显全, 俞春强. 联合压缩感知和颜色向量角的彩色图像哈希方法[J]. 应用科学学报, 2015, 33(6): 595-603.

LIU Kai, TANG Zhen-jun, ZHANG Xian-quan, YU Chun-qiang. Color Image Hashing with Compressive Sensing and Color Vector Angle[J]. Journal of Applied Sciences, 2015, 33(6): 595-603.

参考文献

[1] 唐振军,王朔中,魏为民,苏胜君.用于图像hash的视觉相似度客观评价测度[J].中国图象图形学报, 2008, 13(10):2039-2042. Tang Z J, Wang S Z, Wei W M, Su S J. Perceptual similarity metric for application to robust image hashing[J]. Journal of Image and Graphic, 2008, 13(10):2039-2042. (in Chinese)

[2] Venkatesan R, Koon S, Jakubowski M, Moulin P. Robust image hashing[C]//Proceeding of IEEE International Conference on Image Processing, Vancouver, Canada, 2000:664-666.

[3] Lefebvre F, Macq B, Legat J D. RASH:Radon soft hash algorithm[C]//Proceeding of European Signal Processing Conference, Toulouse, France, 2002:299-302.

[4] Ou Y, Rhee K H. A key-dependent secure image hashing scheme by using Radon transform[C]//Proceedings of the IEEE International Symposium on Intelligent Signal Processing and Communication Systems, Kanazawa, Japan, 2009:595-598.

[5] Kozat S S, Venkatesan R, Mihcak M K. Robust perceptual image hashing via matrix invariants[C]//Proceedings of IEEE International Conference on Image Processing (ICIP'04), Singapore, 2004:3443-3446.

[6] Monga V, Mihcak M K. Robust and secure image hashing via non-negative matrix factorizations[J]. IEEE Transactions on Information Forensics and Security, 2007, 2(3):376-390.

[7] Tang Z, Zhang X, Zhang S. Robust perceptual image hashing based on ring partition and NMF[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(3):711-724.

[8] Li Y, Lu Z, Zhu C, Niu X. Robust image hashing based on random Gabor filtering and dithered lattice vector quantization[J]. IEEE Transactions on Image Processing, 2012, 21(4):1963-1980.

[9] 金秋明,王朔中,李茜,张新鹏.基于角点检测的稳健图像摘要[J].中国图象图形学报, 2008, 13(8):1454-1458. Jin Q M, Wang S Z, Li X, Zhang X P. Robust image hashing based on Harris corner[J]. Journal of Image and Graphic, 2008, 13(8):1454-1458. (in Chinese)

[10] Lü X, Wang Z J. Perceptual image hashing based on shape contexts and local feature points[J]. IEEE Transactions onInformation Forensics and Security, 2012, 7(3):1081-1093.

[11] Tang Z, Dai Y, Zhang X, Zhang S. Perceptual image hashing with histogram of color vector angles[C]//2012 International Conference on Active Media Technology (AMT 2012), Lecture Notes in Computer Science, 2012, 7669:237-246.

[12] Sun R, Zeng W. Secure and robust image hashing via compressive sensing[J]. Multimedia Tools and Applications, 2014, 77(3):1651-1665.

[13] Dony R D, Wesolkowski S. Edge detection on color images using RGB vector angles[C]//Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, Edmonton, Alberta, Canada, 1999, 2:687-692.

[14] Kim N W, Kim T Y, Choi J S. Edge-based spatial descriptor for content-based image retrieval[C]//Lecture Notes in Computer Science, Singapore, 2005, 3568:454-464.

[15] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.

[16] Kang W, LUS, Hsu Y. Compressive sensing-based image hashing[C]//Proceedings of 2009 IEEE International Conference on Image Processing, Cairo, Egypt, 2009:1285-1288.

[17] Gan L, Do T T, Tran T D. Fast compressive imaging using scrambled block Hadamard ensemble[C]//Proceedings of the European Signal Processing Conference, Switzerland, 2008:1-5.

[18] Petitcolas F A P. Watermarking schemes evaluation[J]. IEEE Signal Processing Magazine, 2000, 17(5):58-64.

[19] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874.

联合压缩感知和颜色向量角的彩色图像哈希方法

Color Image Hashing with Compressive Sensing and Color Vector Angle

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