一种基于双JPEG压缩的数字图像篡改的检测方法
|
摘要
随着互联网技术的快速发展,高分辨率数码相机以及功能强大的图像编辑软件的出现,图像篡改已经越来越普遍,它在一定程度上丰富了人们的日常生活,杂志封面和商业广告上精美的图片让人目不暇接。但是篡改图像也带来了许多问题。如果将篡改图像用在新闻媒体或法律上,对社会将会造成很大的影响。因此,对数字图像的真伪鉴别非常重要。而JPEG是目前主流的图像压缩标准,大多数图片是用JPEG格式存储的。在此背景下,本文提出了一种基于双JPEG压缩的数字图像篡改的检测方法。
本文首先介绍了JPEG压缩标准,JPEG编解码原理及其实现过程。同时介绍了双JPEG压缩原理以及几个相关概念。着重分析了单量化、双量化、多次量化后DCT系数直方图的特性,并阐述了双量化后DCT系数直方图周期性产生的原因。
其次,文章提出了一种基于双JPEG压缩的数字图像篡改的检测方法。首先描述了篡改图像、DCT块、篡改块、非篡改块等几个相关的概念,分析了JPEG图像的双量化效果,指出了被篡改的JPEG图像中篡改块与非篡改块可分的依据。并且给出了一种简单有效的估计直方图周期的算法。其次,根据贝叶斯决策理论设计了针对图像中篡改块与非篡改块的两类别分类器,用于检测篡改块。再次,建立了一个包含110幅图像的图像库,对其中一部分图像做篡改之后,用不同的压缩质量因子进行JPEG压缩,将其中60幅输入SVM用作训练数据,另外50幅用作测试数据。文中分析了两次JPEG压缩的质量因子与错误分类个数的对应关系。
最后,给定一幅待检测图像,抽取一个四维特征向量输入到训练好的SVM中检测图像真伪。通过大量实验对该算法进行测试并对实验结果进行分析,总结了不同的压缩质量因子以及篡改图像的尺寸与该方法有效性的关系。实验证明,本文提出的基于双JPEG压缩的数字图像篡改的检测方法是非常有效的。
With the rapid development of internet, the emergence of high resolution digital cameras, and powerful image editing software, doctored images can be found anywhere. It can greatly enrich the users lives. People could see many beautiful images on magazine covers and commercial advertisement. But doctored images may also cause some problems. It will cause great social problems if they are used on news report or court. It is very important to detect the doctored images. JPEG is the most frequently used image format, and most of the images are saved as JPEG format. This paper focus on the method of detecting doctored images based on double JPEG compression effect.
At the beginning, this paper introduces the JPEG still picture compression standard, JPEG coding and decoding method, as well as double JPEG compression, and analyses the effect of DCT coefficient histogram of single quantization, double quantization and multiple quantization, and explains why the double quantization of a signal introduces periodic artifacts.
Secondly, the paper proposes the method of detecting doctored double JPEG images. Several terms are described, The paper analyses the double quantization effect from double JPEG compression, and points out that it is divisible of doctored blocks and undoctored blocks. A simple and effective method used to estimate the period of histogram has been implemented. The paper designs a classifier of two classes using Bayesian decision theory to segment the doctored blocks and the undoctored blocks. A library containing 110 images has been built for training and testing the SVM. The images are doctored first, and then compressed by different compression factors.
The paper analyses the relationship between the compression factors(q1 and q2) and error numbers. Finally, given a image, a four-dimensional feature vector has been extracted, and then feed it into the trained SVM to decide whether the image is doctored. Lots of images are used to test this algorithm. The paper comes to the conclusion that double compression factors and images sizes are closely associated with the effectiveness of this algorithm. The algorithm is effective with the experiments.
本文首先介绍了JPEG压缩标准,JPEG编解码原理及其实现过程。同时介绍了双JPEG压缩原理以及几个相关概念。着重分析了单量化、双量化、多次量化后DCT系数直方图的特性,并阐述了双量化后DCT系数直方图周期性产生的原因。
其次,文章提出了一种基于双JPEG压缩的数字图像篡改的检测方法。首先描述了篡改图像、DCT块、篡改块、非篡改块等几个相关的概念,分析了JPEG图像的双量化效果,指出了被篡改的JPEG图像中篡改块与非篡改块可分的依据。并且给出了一种简单有效的估计直方图周期的算法。其次,根据贝叶斯决策理论设计了针对图像中篡改块与非篡改块的两类别分类器,用于检测篡改块。再次,建立了一个包含110幅图像的图像库,对其中一部分图像做篡改之后,用不同的压缩质量因子进行JPEG压缩,将其中60幅输入SVM用作训练数据,另外50幅用作测试数据。文中分析了两次JPEG压缩的质量因子与错误分类个数的对应关系。
最后,给定一幅待检测图像,抽取一个四维特征向量输入到训练好的SVM中检测图像真伪。通过大量实验对该算法进行测试并对实验结果进行分析,总结了不同的压缩质量因子以及篡改图像的尺寸与该方法有效性的关系。实验证明,本文提出的基于双JPEG压缩的数字图像篡改的检测方法是非常有效的。
With the rapid development of internet, the emergence of high resolution digital cameras, and powerful image editing software, doctored images can be found anywhere. It can greatly enrich the users lives. People could see many beautiful images on magazine covers and commercial advertisement. But doctored images may also cause some problems. It will cause great social problems if they are used on news report or court. It is very important to detect the doctored images. JPEG is the most frequently used image format, and most of the images are saved as JPEG format. This paper focus on the method of detecting doctored images based on double JPEG compression effect.
At the beginning, this paper introduces the JPEG still picture compression standard, JPEG coding and decoding method, as well as double JPEG compression, and analyses the effect of DCT coefficient histogram of single quantization, double quantization and multiple quantization, and explains why the double quantization of a signal introduces periodic artifacts.
Secondly, the paper proposes the method of detecting doctored double JPEG images. Several terms are described, The paper analyses the double quantization effect from double JPEG compression, and points out that it is divisible of doctored blocks and undoctored blocks. A simple and effective method used to estimate the period of histogram has been implemented. The paper designs a classifier of two classes using Bayesian decision theory to segment the doctored blocks and the undoctored blocks. A library containing 110 images has been built for training and testing the SVM. The images are doctored first, and then compressed by different compression factors.
The paper analyses the relationship between the compression factors(q1 and q2) and error numbers. Finally, given a image, a four-dimensional feature vector has been extracted, and then feed it into the trained SVM to decide whether the image is doctored. Lots of images are used to test this algorithm. The paper comes to the conclusion that double compression factors and images sizes are closely associated with the effectiveness of this algorithm. The algorithm is effective with the experiments.
引文
[1] Willam B Pennebaker, Joan L Mitchell著.黎洪松,成实译.JPEG静止图像数据压缩标准[S].北京:学苑出版社,1996.
[2] Stefan K., Fabien A.P.P.吴秋新等译.信息隐藏技术一隐写术与数字水印[M].北京:人民邮电出版社,2001.
[3] Ingemar J. Cox, Matthew L. Miller, and Jeffrey A. Bloom著,王颖,黄志蓓等译.数字水印[M]北京:电子工业出版社,2003.
[4] 王艳,可自恢复的图像篡改认证算法研究[D],合肥工业大学,硕士,2006.3, pp.6-19.
[5] Tirkel A Z, et al. Electronic watermark[A]. Digital image computing Technology and Applications[C], 1993,Macquarie University, 1:666-673.
[6] Van Schyndel R., Tirkel, A., and Osborne, C. A digital watermark[A]. In Proceedings of ICIP (Austin, Tex., Nov)IEEE Press[C], 1994, pp.86-90.
[7] Ingemar J. Cox, Joe Killian F, Thomson, and Talal Shamoon. Secure Spread Spectrum Watermarking for Multimedia[J]. IEEE Trans. On Image Processing, 6(12), 1997, pp.1673-1687.
[8] Pitas I. A method for signature casting on digital images[A]. IEEE International Conference on Image Processing[C],1996,3:215-218.
[9] P. W. Wong. A watermark for image integrity and ownership verification[C]. In Proceedings of IS&T PIC Conference, Portland, Oregon, 1998.5, pp.374-379.
[10] J. Fridrich. Security of fragile authentication watermarks with localization[C]. In Proceedings of SPIE, Electronic Imaging 2002, Security and Watermarking of Multimedia Contents, volume 4675, 2002.1, pp.691-700.
[11] M. U. Celik, G. Sharma, E. Saber, and A. M. Tekalp. Hierarchical watermarking for secure image authentication with localization[J]. IEEE Transactions on Image Processing, 11(6), 2002.6, pp.585-595.
[12] M. Yeung and F. Mintzer. An invisible watermarking technique for image verification[C]. In Proceedings of the International Conference on Image Processing, volume 1, 1997, pp.680-683.
[13] Johnson M.K. and Farid H. Exposing Digital Forgeries by Detecting Inconsistencies in Lighting[J]. Proc. ACM Multimedia and Security Workshop, New York, 2005, pp.1-9.
[14] Fridrich J., Soukal D., and Luká? J. Detection of Copy-Move Forgery in Digital Images[J], Proc. Digital Forensic Research Workshop, Cleveland, OH, 2003.8.
[15] The Joint Photographic Experts Group, T0081E[M] , 1992, pp.1-118.
[16] 张益真.刘滔编著.Visual C++实现MPEG/JPEG编解码技术[M].北京:北京邮电出版社, 2002, pp.1-110.
[17] G.K.Wallace. The JPEG still picture compression standard[S]. IEEE Transactions on Consumer Electronics, 34(4):30-44, 1991.
[18] 罗俊松.基于JPEG2000标准的数字图像压缩的算法研究[D].成都理工大学,硕士,2003.4, pp.29-33.
[19] Fan Z. and de Queiroz R. L. Identification of Bitmap Compression History: JPEG Detection and Quantizer Estimation[C]. in IEEE Transactions on Image Processing, 12(2), 2003.2,pp.230–235.
[20] 方敏.JPEG算法的研究与实现[D].辽宁工程技术大学,硕士, 2002.11, pp.1-5.
[21] D. Hankerson, G. A. Harris, and P. D. Johnson Jr.Introduction to Information Theory and Data Compression[M].Boca Raton,FL:CRC,1997.
[22] J. R. Price and M. Rabbani. Biased reconstruction for JPEG decoding[J]. IEEE Signal Processing Lett., 1999.12,vol. 6, pp. 297–299.
[23] A. C. Popescu and H. Farid. Statistical Tools for Digital Forensics[R]. 6th International Workshop on Information Hiding, Toronto, Canada, 2004.
[24] R. C. Reininger and J. D. Gibson. Distributions of the two-dimensional DCT coefficients for images[J]. IEEE Trans. Commun., vol. COM-31, 1983.6, pp.835-839.
[25] E. Y. Lam and J.W. Goodman. A mathematical analysis of the DCT coefficient distributions for images[J]. IEEE Trans. Image Processing, vol.9,2000.10. pp.1661-1666.
[26] S. R. Smoot and L. A. Rowe. Laplacian model for AC DCT terms in image and video coding[J]. Proc. 9th IEEE Image and Multidimension Digital Signal Processing Workshop, 1996.
[27] Stephen R. Smoot. Study of DCT coefficient distribution[J]. in Human Vision and Electronic Imaging, B. E. Rogowitz and T. N. Pappas, Eds. Bellingham, WA: SPIE, 1996, vol. 26-57.
[28] Junfeng He,Zhouchen Lin,Lifeng Wang,Xiaoou Tang.Detecting Doctored JPEG Images via DCT Coefficient Analysis[C].ECCV,2006.
[29] Jan Luká? and Jessica Fridrich. Estimation of Primary Quantization Matrix in Double Compressed JPEG Images[C].In Digital Forensic Research Workshop, Cleveland, Ohio, 2003.8.
[30] 李金宗编著.模式识别导论[M].北京:高等教育出版社,1994.
[31] 钟珞,潘昊,封筠,和平等编著.模式识别[M].武汉大学出版社,2006,pp.6-50.
[32] 侯小静.贝叶斯分类器研究及其在Web文档分类中的应用[D].郑州大学,硕士,2005.5, pp.8-15.
[33] R.Duda and P. Hart. Pattern Classification and Scene Analysis[M]. John Wiley and Sons, 1973.
[34] Standard Test Images[OL]. http://links.uwaterloo.ca/bragzone.base.html.
[35] Kohavi.R,John,G.H.Wrappers for Feature Subset Selection[C]. Artificial Intelligence,1997, pp.273-3240.
[36] Mladernnic.D, Grobelnik.M. Feature Selection for unbalanced class distribution and Naive Bayes[R]. In Proceedings of the Sixteenth International Conference on Machine teaming. Bled, Morgan Kaufmann, 1999, pp.258-267.
[37] 李晶皎,朱志良,王爱侠等译.Sergios Theodoridis等著.模式识别(第二版)[M].电子工业出版社,2004,pp.106-202.
[38] 边肇祺,张学工等编著.模式识别(第二版)[M].北京:清华大学出版社,2000, pp.296-303.
[39] 关新平编著.信号处理与模式式别[M].北京:机械工业出版社,2006,pp.58-64.
[40] Chih-Chung Chang and Chih-Jen Lin.LIBSVM:a library for support vector machines[M]. 2001.
[41] C.W. Hsu and C.J. Lin. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks, 13(2002), pp.415-425.
[42] 李 宏 东 , 姚 天 翔 等 译 .Richard O.Duda 等 著 . 模 式 分 类 [M]. 北 京 : 机 械 工 业 出 版社,2003,pp.96-101.
[2] Stefan K., Fabien A.P.P.吴秋新等译.信息隐藏技术一隐写术与数字水印[M].北京:人民邮电出版社,2001.
[3] Ingemar J. Cox, Matthew L. Miller, and Jeffrey A. Bloom著,王颖,黄志蓓等译.数字水印[M]北京:电子工业出版社,2003.
[4] 王艳,可自恢复的图像篡改认证算法研究[D],合肥工业大学,硕士,2006.3, pp.6-19.
[5] Tirkel A Z, et al. Electronic watermark[A]. Digital image computing Technology and Applications[C], 1993,Macquarie University, 1:666-673.
[6] Van Schyndel R., Tirkel, A., and Osborne, C. A digital watermark[A]. In Proceedings of ICIP (Austin, Tex., Nov)IEEE Press[C], 1994, pp.86-90.
[7] Ingemar J. Cox, Joe Killian F, Thomson, and Talal Shamoon. Secure Spread Spectrum Watermarking for Multimedia[J]. IEEE Trans. On Image Processing, 6(12), 1997, pp.1673-1687.
[8] Pitas I. A method for signature casting on digital images[A]. IEEE International Conference on Image Processing[C],1996,3:215-218.
[9] P. W. Wong. A watermark for image integrity and ownership verification[C]. In Proceedings of IS&T PIC Conference, Portland, Oregon, 1998.5, pp.374-379.
[10] J. Fridrich. Security of fragile authentication watermarks with localization[C]. In Proceedings of SPIE, Electronic Imaging 2002, Security and Watermarking of Multimedia Contents, volume 4675, 2002.1, pp.691-700.
[11] M. U. Celik, G. Sharma, E. Saber, and A. M. Tekalp. Hierarchical watermarking for secure image authentication with localization[J]. IEEE Transactions on Image Processing, 11(6), 2002.6, pp.585-595.
[12] M. Yeung and F. Mintzer. An invisible watermarking technique for image verification[C]. In Proceedings of the International Conference on Image Processing, volume 1, 1997, pp.680-683.
[13] Johnson M.K. and Farid H. Exposing Digital Forgeries by Detecting Inconsistencies in Lighting[J]. Proc. ACM Multimedia and Security Workshop, New York, 2005, pp.1-9.
[14] Fridrich J., Soukal D., and Luká? J. Detection of Copy-Move Forgery in Digital Images[J], Proc. Digital Forensic Research Workshop, Cleveland, OH, 2003.8.
[15] The Joint Photographic Experts Group, T0081E[M] , 1992, pp.1-118.
[16] 张益真.刘滔编著.Visual C++实现MPEG/JPEG编解码技术[M].北京:北京邮电出版社, 2002, pp.1-110.
[17] G.K.Wallace. The JPEG still picture compression standard[S]. IEEE Transactions on Consumer Electronics, 34(4):30-44, 1991.
[18] 罗俊松.基于JPEG2000标准的数字图像压缩的算法研究[D].成都理工大学,硕士,2003.4, pp.29-33.
[19] Fan Z. and de Queiroz R. L. Identification of Bitmap Compression History: JPEG Detection and Quantizer Estimation[C]. in IEEE Transactions on Image Processing, 12(2), 2003.2,pp.230–235.
[20] 方敏.JPEG算法的研究与实现[D].辽宁工程技术大学,硕士, 2002.11, pp.1-5.
[21] D. Hankerson, G. A. Harris, and P. D. Johnson Jr.Introduction to Information Theory and Data Compression[M].Boca Raton,FL:CRC,1997.
[22] J. R. Price and M. Rabbani. Biased reconstruction for JPEG decoding[J]. IEEE Signal Processing Lett., 1999.12,vol. 6, pp. 297–299.
[23] A. C. Popescu and H. Farid. Statistical Tools for Digital Forensics[R]. 6th International Workshop on Information Hiding, Toronto, Canada, 2004.
[24] R. C. Reininger and J. D. Gibson. Distributions of the two-dimensional DCT coefficients for images[J]. IEEE Trans. Commun., vol. COM-31, 1983.6, pp.835-839.
[25] E. Y. Lam and J.W. Goodman. A mathematical analysis of the DCT coefficient distributions for images[J]. IEEE Trans. Image Processing, vol.9,2000.10. pp.1661-1666.
[26] S. R. Smoot and L. A. Rowe. Laplacian model for AC DCT terms in image and video coding[J]. Proc. 9th IEEE Image and Multidimension Digital Signal Processing Workshop, 1996.
[27] Stephen R. Smoot. Study of DCT coefficient distribution[J]. in Human Vision and Electronic Imaging, B. E. Rogowitz and T. N. Pappas, Eds. Bellingham, WA: SPIE, 1996, vol. 26-57.
[28] Junfeng He,Zhouchen Lin,Lifeng Wang,Xiaoou Tang.Detecting Doctored JPEG Images via DCT Coefficient Analysis[C].ECCV,2006.
[29] Jan Luká? and Jessica Fridrich. Estimation of Primary Quantization Matrix in Double Compressed JPEG Images[C].In Digital Forensic Research Workshop, Cleveland, Ohio, 2003.8.
[30] 李金宗编著.模式识别导论[M].北京:高等教育出版社,1994.
[31] 钟珞,潘昊,封筠,和平等编著.模式识别[M].武汉大学出版社,2006,pp.6-50.
[32] 侯小静.贝叶斯分类器研究及其在Web文档分类中的应用[D].郑州大学,硕士,2005.5, pp.8-15.
[33] R.Duda and P. Hart. Pattern Classification and Scene Analysis[M]. John Wiley and Sons, 1973.
[34] Standard Test Images[OL]. http://links.uwaterloo.ca/bragzone.base.html.
[35] Kohavi.R,John,G.H.Wrappers for Feature Subset Selection[C]. Artificial Intelligence,1997, pp.273-3240.
[36] Mladernnic.D, Grobelnik.M. Feature Selection for unbalanced class distribution and Naive Bayes[R]. In Proceedings of the Sixteenth International Conference on Machine teaming. Bled, Morgan Kaufmann, 1999, pp.258-267.
[37] 李晶皎,朱志良,王爱侠等译.Sergios Theodoridis等著.模式识别(第二版)[M].电子工业出版社,2004,pp.106-202.
[38] 边肇祺,张学工等编著.模式识别(第二版)[M].北京:清华大学出版社,2000, pp.296-303.
[39] 关新平编著.信号处理与模式式别[M].北京:机械工业出版社,2006,pp.58-64.
[40] Chih-Chung Chang and Chih-Jen Lin.LIBSVM:a library for support vector machines[M]. 2001.
[41] C.W. Hsu and C.J. Lin. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks, 13(2002), pp.415-425.
[42] 李 宏 东 , 姚 天 翔 等 译 .Richard O.Duda 等 著 . 模 式 分 类 [M]. 北 京 : 机 械 工 业 出 版社,2003,pp.96-101.