基于小波系数规则性的JPEG2000图像篡改检测
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摘要
近年来,随着计算机网络通信技术的飞速发展,各种形式的数字媒体得到了广泛的应用。但是,由于功能强大的图像处理软件的广泛使用,数字图像越来越容易被篡改。传统上,在鉴别图像真伪时,采用主动检测的方法,即:事先在数字图像中嵌入数字水印或签名的方法。但是,该方法存在着诸多的缺陷和不足。因此,仅根据图像自身进行图像真实性和完整性认证的被动检测方法成为了新兴的热门研究方向。
在通过对图像进行拷贝、粘贴等操作而达到图像篡改目的的过程中,往往会在图像篡改部分与原图像的接合处出现瑕疵(奇异)点。而小波变换系数模的局部极大值与图像的奇异点(突变点)有着特定的联系,因此,可以据此实现对图像的篡改检测。
本论文根据小波系数的规则性,提出了一种JPEG2000图像篡改的被动检测方法。其算法原理是:小波系数的模取值越大,那么,小波变换尺度和小波系数模的局部极大值的对数就越近似呈线性关系。该方法首先提取图像的边缘信息,然后采用二进小波变换获取图像各子带中对应像素点的信息,最后对小波系数模的局部极大值的对数进行一维曲线拟合,从而检测图像是否经过篡改,并对篡改部分进行定位。实验结果表明,本算法对JPEG2000图像有较好的检测效果,可以较为准确地确定篡改部位。另外,本算法检测效率较高,运行时间较短,因此,它是一种效率和效果兼顾的数字图像被动检测算法。
Nowadays, with the rapid development of computer network and communication technology, various forms of digital media have been widely used. However, due to the widespread use of powerful image-processing software, it is increasingly easier to tamper digital images. The traditional method to distinguish trueness or fake is to embed digital watermarks or signatures in digital images in advance. But it still has a lot of weaknesses. Therefore it forms a new and important research direction to authenticate digital images without watermarks or signatures, which is called passive image detection.
In the course of image forgery with paste-copy, forgers often produce some singularity points at the edge of image junction. A specific contact between the logarithm of local maximum value of wavelet transform coefficient and the singularity points of image can be used to achieve the tamper detection of images.
This dissertation presents a new JPEG2000 image tamper detection measure based on regularity of wavelet coefficients. Its algorithm principle is: Firstly, this method extracts the image edge information. Then it accesses to the corresponding pixels of information on the image sub-bands with UDWT (Undecimated Discrete Wavelet Transform). Finally, curve fitting is used. The experimental results show that the algorithm can detect JPEG 2000 tampered image ideally. In addition, this method can be used effectively.
在通过对图像进行拷贝、粘贴等操作而达到图像篡改目的的过程中,往往会在图像篡改部分与原图像的接合处出现瑕疵(奇异)点。而小波变换系数模的局部极大值与图像的奇异点(突变点)有着特定的联系,因此,可以据此实现对图像的篡改检测。
本论文根据小波系数的规则性,提出了一种JPEG2000图像篡改的被动检测方法。其算法原理是:小波系数的模取值越大,那么,小波变换尺度和小波系数模的局部极大值的对数就越近似呈线性关系。该方法首先提取图像的边缘信息,然后采用二进小波变换获取图像各子带中对应像素点的信息,最后对小波系数模的局部极大值的对数进行一维曲线拟合,从而检测图像是否经过篡改,并对篡改部分进行定位。实验结果表明,本算法对JPEG2000图像有较好的检测效果,可以较为准确地确定篡改部位。另外,本算法检测效率较高,运行时间较短,因此,它是一种效率和效果兼顾的数字图像被动检测算法。
Nowadays, with the rapid development of computer network and communication technology, various forms of digital media have been widely used. However, due to the widespread use of powerful image-processing software, it is increasingly easier to tamper digital images. The traditional method to distinguish trueness or fake is to embed digital watermarks or signatures in digital images in advance. But it still has a lot of weaknesses. Therefore it forms a new and important research direction to authenticate digital images without watermarks or signatures, which is called passive image detection.
In the course of image forgery with paste-copy, forgers often produce some singularity points at the edge of image junction. A specific contact between the logarithm of local maximum value of wavelet transform coefficient and the singularity points of image can be used to achieve the tamper detection of images.
This dissertation presents a new JPEG2000 image tamper detection measure based on regularity of wavelet coefficients. Its algorithm principle is: Firstly, this method extracts the image edge information. Then it accesses to the corresponding pixels of information on the image sub-bands with UDWT (Undecimated Discrete Wavelet Transform). Finally, curve fitting is used. The experimental results show that the algorithm can detect JPEG 2000 tampered image ideally. In addition, this method can be used effectively.
引文
[1] M. K. Johnson and H. Farid, Exposing digital forgeries by detecting inconsistencies in lighting, ACM Multimedia and Security Workshop, New York, NY, 2005.
[2] M.K. Johnson and H. Farid, Exposing Digital Forgeries in Complex Lighting Environments, IEEE Transactions on Information Forensics and Security, 2(3):450-461, 2007.
[3] M.K. Johnson and H. Farid, Exposing Digital Forgeries Through Specular Highlights on the Eye, 9th International Workshop on Information Hiding, Saint Malo, France, 2007.
[4] M.K Johnson and H. Farid, Detecting Photographic Composites of People, 6th International Workshop on Digital Watermarking, Guangzhou, China, 2007.
[5] Jung-Beom Lee,Yong-In Yoon, Kyoung-Soo Doo, Dong-Hwan Har; Jong-Soo Choi, Detecting digital forgeries using of a lighting direction .Consumer Electronics, 2007. ICCE 2007. Digest of Technical Papers. International Conference on10-14 Jan. 2007 Page(s):1– 2
[6] M. K. Johnson and H. Farid, Exposing Digital Forgeries Through Chromatic Aberration, ACM Multimedia and Security Workshop, Geneva, Switzerland, 2006.
[7] A.C. Popescu and H. Farid, Exposing Digital Forgeries in Color Filter Array Interpolated Images, IEEE Transactions on Signal Processing, 53(10):3948-3959, 2005.
[8] Imaging Sensor Noise as Digital X-Ray for Revealing Forgeries, with Mo Chen and M. Goljan, Proc. of 9th Information Hiding Workshop, Saint Malo, France, June 11-13, 2007 (to appear in LNCS).
[9] Z. Lin, R. Wang, X. Tang, and H.-Y. Shum, Detecting doctored images using camera response normality and consistency, in CVPR, pp. 1087–1092, 2005
[10] Lin, J. Gu, S Yamazaki, and H.-Y. Shum. Radiometric Calibration from a Single Image. Proceedings of CVPR 2004,pp.938-945
[11] Y.-F. Hsu and S.-F. Chang, Detecting Image Splicing Using Geometry Invariants and Camera Characteristics Consistency, In ICME, Toronto, Canada, July 2006
[12] Y.-F. Hsu and S.-F. Chang, Image Splicing Detection Using Camera Response Function Consistency and Automatic Segmentation, IEEE International Conference on Multimedia and Expo, page(s): 28-31, 2-5 July 2007.
[13]武伟,詹玲超,利用颜色滤波阵列特性和模糊估计检测篡改,计算机工程与设计,2007.11,28(21):5179-5181
[14] H.Farid.Detecting digital forgeries using bispectral analysis. Technical Report AIM-1657,Massachusetts Institue of Technology,August 1999.
[15] T.-T.Ng and S.-F.Chang.Blind detection of digital photomontage using higher order statistics.Technical Report ADVENT 201-2004-1,Columbia University,June 2004.
[16] A.C.Popescu and H.Farid.statistical tools for digital forensics.In Proceedings of the 6th Information Hiding Workshop,May 2004.
[17] Shuiming Ye,Qibin Sun,Ee-Chien Chang.Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact.Multimedia and Expo,2007 IEEE International Conference on 2-5 July 2007.P12-15.
[18] S.Lyu and H.Farid.How realistic is photorealistic? IEEE Transaction on Signal Processing.vol.53,No.2,February 2005.
[19] W.Chen, Y.Q.Shi, W.SU, Image Splicing Detection Using 2-D Phase Congruency and Statistical Moments of Characteristic Function, SPIE Electronic Imaging: Security, Steganography and Watermarking of Multimedia Contents, San Jose, CA, USA, January 2007.
[20] C.Chen, Y.Q.Shi, G.Xuan, Steganalyzing Texture Images, IEEE ICIP2007.
[21] Y.Q.Shi,Statistical Model for Digital Image Forensics,第七届全国信息隐藏暨多媒体信息安全学术大会特邀报告,南京,2007.
[22] W.Chen, Y.Q.Shi, G.Xuan, W.SU, Steganalysis versus splicing detection, 6th International Workshop on Digital Watermarking,Guangzhou, China, 2007.
[23]Junfeng He, Zhouchen Lin, LifengWang, and Xiaoou Tang, Detecting Doctored JPEG Images Via DCT Coefficient Analysis, ECCV 2006, Part III, LNCS 3953, pp. 423–435, 2006.
[24]J. Lukas and J. Fridrich, Estimation of Primary Quantization Matrix in Double Compressed JPEG Images, Proc. Digital Forensic Research Workshop 2003. Cleveland, OH, USA, August 5-8.
[25]Weiqi Luo,Zhenhua Qu,Jiwu Huang,et al. A Novel Method for Detecting Cropped and Recompressed Image Block. IEEE Proc of InternationalConference on Acoustics,Speech and Signal Processing, 217-220, 2007.
[26]A.C. Popescu and H. Farid, Exposing Digital Forgeries by Detecting Traces of Re-sampling, IEEE Transactions on Signal Processing, 53(2):758-767, 2005.
[27]朱秀明,宣国荣,姚秋明等,基于EM算法的数字取证中图像重采样检测,哈尔滨工业大学学报,2006,38(A):868-871.
[28] J. Fridrich, D. Soukal and J. Lukas, Detection of Copy-Move Forgery in Digital Images, Proc. of DFRWS 2003, Cleveland, OH, USA, August 5-8 2003.
[29] A.C. Popescu and H. Farid, Exposing Digital Forgeries by Detecting Duplicated Image Regions, Technical Report, TR2004-515, Dartmouth College, Computer Science.
[30] Guohui LiI, Qiong WuI, Dan TuI, Shao/ie SunI, A sorted neighborhood approach for detecting for detecting duplicated regions in image forgeries based on DWT and SVD, ICME:1750-1753, 2007.
[31]魏为民王朔中唐振军师彪,一类数字图像篡改的被动认证,第七届全国信息隐藏暨多媒体信息安全学术大会会议论文集,南京,238-241.
[32] H. Farid, Exposing Digital Forgeries in Scientific Images, ACM Multimedia and Security Workshop, Geneva, Switzerland, 2006.
[33]骆伟祺,黄继武,丘国平,鲁棒的区域复制图像篡改检测技术,计算机学报,2007,30(11):1998-2007.
[34] D. Y. Hsiao,S. C.Pei, Detecting Digital Tampering by Blur Estimation. Proc of the First International Workshop on Systematic Approach to Digital Forensic Engineering. IEEE Computer Soc, 264-278. 2005.
[35]王波,孙璐璐,孔祥维等,图像伪造中模糊操作的异常色调率取证技术,电子学报,2006,34(12A):2451-2454.
[36]王经民编著,小波分析,杨凌:西北农林科技大学出版社,2004.4,23-52
[37]魏明果编著,实用小波分析,北京:北京理工大学出版社,2005.9,16-130
[38]王大凯,彭进业编著,小波分析及其在信号处理中的应用,北京:电子工业出版社,2006.1,24-56,133-160
[39]张国华,张文娟,薛鹏翔编著,小波分析与应用基础,西安:西北工业大学出版社,2006.8,14-142
[40]李弼程,罗建书编著,小波分析及其应用,北京:电子工业出版社,2003.5,9-41,130-144
[41]陈基明编著,小波分析基础,上海:上海大学出版社,2002.12,1-148
[42] Sutcu, Y.; Coskun, B.; Sencar, H.T.; Memon, N.. Tamper Detection Based on Regularity of Wavelet Transform Coefficients (Extended Abstract), Image Processing, 2007. ICIP 2007. IEEE International Conference on Volume 1, Sept. 16 2007-Oct. 19 2007 Page(s):I - 397 - I– 400
[43] W.K.Carey,D.B.Chuang,and S.S.Hemami. Regularity-preserving image interpolation,IEEE Transactions on Image Processing,vol.8,pp.1293-1297,1999
[44] Rooms F.,Pizurca A.,and Philips W..Estimating image blur in the wavelet domain,ICASSP 2002,2002
[45]石振东,刘国庆编著,实验数据处理与曲线拟合技术,哈尔滨:哈尔滨船舶工程学院出版社,1991.12,184-238
[46]李道本编著,信号的统计检测与估计理论,北京:科学出版社,2004.09,42-45
[2] M.K. Johnson and H. Farid, Exposing Digital Forgeries in Complex Lighting Environments, IEEE Transactions on Information Forensics and Security, 2(3):450-461, 2007.
[3] M.K. Johnson and H. Farid, Exposing Digital Forgeries Through Specular Highlights on the Eye, 9th International Workshop on Information Hiding, Saint Malo, France, 2007.
[4] M.K Johnson and H. Farid, Detecting Photographic Composites of People, 6th International Workshop on Digital Watermarking, Guangzhou, China, 2007.
[5] Jung-Beom Lee,Yong-In Yoon, Kyoung-Soo Doo, Dong-Hwan Har; Jong-Soo Choi, Detecting digital forgeries using of a lighting direction .Consumer Electronics, 2007. ICCE 2007. Digest of Technical Papers. International Conference on10-14 Jan. 2007 Page(s):1– 2
[6] M. K. Johnson and H. Farid, Exposing Digital Forgeries Through Chromatic Aberration, ACM Multimedia and Security Workshop, Geneva, Switzerland, 2006.
[7] A.C. Popescu and H. Farid, Exposing Digital Forgeries in Color Filter Array Interpolated Images, IEEE Transactions on Signal Processing, 53(10):3948-3959, 2005.
[8] Imaging Sensor Noise as Digital X-Ray for Revealing Forgeries, with Mo Chen and M. Goljan, Proc. of 9th Information Hiding Workshop, Saint Malo, France, June 11-13, 2007 (to appear in LNCS).
[9] Z. Lin, R. Wang, X. Tang, and H.-Y. Shum, Detecting doctored images using camera response normality and consistency, in CVPR, pp. 1087–1092, 2005
[10] Lin, J. Gu, S Yamazaki, and H.-Y. Shum. Radiometric Calibration from a Single Image. Proceedings of CVPR 2004,pp.938-945
[11] Y.-F. Hsu and S.-F. Chang, Detecting Image Splicing Using Geometry Invariants and Camera Characteristics Consistency, In ICME, Toronto, Canada, July 2006
[12] Y.-F. Hsu and S.-F. Chang, Image Splicing Detection Using Camera Response Function Consistency and Automatic Segmentation, IEEE International Conference on Multimedia and Expo, page(s): 28-31, 2-5 July 2007.
[13]武伟,詹玲超,利用颜色滤波阵列特性和模糊估计检测篡改,计算机工程与设计,2007.11,28(21):5179-5181
[14] H.Farid.Detecting digital forgeries using bispectral analysis. Technical Report AIM-1657,Massachusetts Institue of Technology,August 1999.
[15] T.-T.Ng and S.-F.Chang.Blind detection of digital photomontage using higher order statistics.Technical Report ADVENT 201-2004-1,Columbia University,June 2004.
[16] A.C.Popescu and H.Farid.statistical tools for digital forensics.In Proceedings of the 6th Information Hiding Workshop,May 2004.
[17] Shuiming Ye,Qibin Sun,Ee-Chien Chang.Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact.Multimedia and Expo,2007 IEEE International Conference on 2-5 July 2007.P12-15.
[18] S.Lyu and H.Farid.How realistic is photorealistic? IEEE Transaction on Signal Processing.vol.53,No.2,February 2005.
[19] W.Chen, Y.Q.Shi, W.SU, Image Splicing Detection Using 2-D Phase Congruency and Statistical Moments of Characteristic Function, SPIE Electronic Imaging: Security, Steganography and Watermarking of Multimedia Contents, San Jose, CA, USA, January 2007.
[20] C.Chen, Y.Q.Shi, G.Xuan, Steganalyzing Texture Images, IEEE ICIP2007.
[21] Y.Q.Shi,Statistical Model for Digital Image Forensics,第七届全国信息隐藏暨多媒体信息安全学术大会特邀报告,南京,2007.
[22] W.Chen, Y.Q.Shi, G.Xuan, W.SU, Steganalysis versus splicing detection, 6th International Workshop on Digital Watermarking,Guangzhou, China, 2007.
[23]Junfeng He, Zhouchen Lin, LifengWang, and Xiaoou Tang, Detecting Doctored JPEG Images Via DCT Coefficient Analysis, ECCV 2006, Part III, LNCS 3953, pp. 423–435, 2006.
[24]J. Lukas and J. Fridrich, Estimation of Primary Quantization Matrix in Double Compressed JPEG Images, Proc. Digital Forensic Research Workshop 2003. Cleveland, OH, USA, August 5-8.
[25]Weiqi Luo,Zhenhua Qu,Jiwu Huang,et al. A Novel Method for Detecting Cropped and Recompressed Image Block. IEEE Proc of InternationalConference on Acoustics,Speech and Signal Processing, 217-220, 2007.
[26]A.C. Popescu and H. Farid, Exposing Digital Forgeries by Detecting Traces of Re-sampling, IEEE Transactions on Signal Processing, 53(2):758-767, 2005.
[27]朱秀明,宣国荣,姚秋明等,基于EM算法的数字取证中图像重采样检测,哈尔滨工业大学学报,2006,38(A):868-871.
[28] J. Fridrich, D. Soukal and J. Lukas, Detection of Copy-Move Forgery in Digital Images, Proc. of DFRWS 2003, Cleveland, OH, USA, August 5-8 2003.
[29] A.C. Popescu and H. Farid, Exposing Digital Forgeries by Detecting Duplicated Image Regions, Technical Report, TR2004-515, Dartmouth College, Computer Science.
[30] Guohui LiI, Qiong WuI, Dan TuI, Shao/ie SunI, A sorted neighborhood approach for detecting for detecting duplicated regions in image forgeries based on DWT and SVD, ICME:1750-1753, 2007.
[31]魏为民王朔中唐振军师彪,一类数字图像篡改的被动认证,第七届全国信息隐藏暨多媒体信息安全学术大会会议论文集,南京,238-241.
[32] H. Farid, Exposing Digital Forgeries in Scientific Images, ACM Multimedia and Security Workshop, Geneva, Switzerland, 2006.
[33]骆伟祺,黄继武,丘国平,鲁棒的区域复制图像篡改检测技术,计算机学报,2007,30(11):1998-2007.
[34] D. Y. Hsiao,S. C.Pei, Detecting Digital Tampering by Blur Estimation. Proc of the First International Workshop on Systematic Approach to Digital Forensic Engineering. IEEE Computer Soc, 264-278. 2005.
[35]王波,孙璐璐,孔祥维等,图像伪造中模糊操作的异常色调率取证技术,电子学报,2006,34(12A):2451-2454.
[36]王经民编著,小波分析,杨凌:西北农林科技大学出版社,2004.4,23-52
[37]魏明果编著,实用小波分析,北京:北京理工大学出版社,2005.9,16-130
[38]王大凯,彭进业编著,小波分析及其在信号处理中的应用,北京:电子工业出版社,2006.1,24-56,133-160
[39]张国华,张文娟,薛鹏翔编著,小波分析与应用基础,西安:西北工业大学出版社,2006.8,14-142
[40]李弼程,罗建书编著,小波分析及其应用,北京:电子工业出版社,2003.5,9-41,130-144
[41]陈基明编著,小波分析基础,上海:上海大学出版社,2002.12,1-148
[42] Sutcu, Y.; Coskun, B.; Sencar, H.T.; Memon, N.. Tamper Detection Based on Regularity of Wavelet Transform Coefficients (Extended Abstract), Image Processing, 2007. ICIP 2007. IEEE International Conference on Volume 1, Sept. 16 2007-Oct. 19 2007 Page(s):I - 397 - I– 400
[43] W.K.Carey,D.B.Chuang,and S.S.Hemami. Regularity-preserving image interpolation,IEEE Transactions on Image Processing,vol.8,pp.1293-1297,1999
[44] Rooms F.,Pizurca A.,and Philips W..Estimating image blur in the wavelet domain,ICASSP 2002,2002
[45]石振东,刘国庆编著,实验数据处理与曲线拟合技术,哈尔滨:哈尔滨船舶工程学院出版社,1991.12,184-238
[46]李道本编著,信号的统计检测与估计理论,北京:科学出版社,2004.09,42-45