基于高光去除的打印图像相机来源取证技术
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摘要
照片作为生成证据的一种,在刑侦过程和司法体系中扮演着非常重要的角色。照片一般会以数字图像和打印图像两种形式递交法庭,在确认照片可以作为法庭的有效证据之前需要对其来源与真实性进行认证。以此为应用背景,图像取证领域的学者们一直把数字图像相机来源认证作为一个重要的研究方向,但是对于打印照片的相机来源认证却鲜有研究成果问世。
事实上在很多时候是无法获得原始数字图像的,例如照片的提供者只保留了数码照片的打印版本而删除了原始数字图像,或者提供的数字图像本身就是经过扫描获取到的等等。因此,如何能够确保这类图像来源的真实性和可靠性,也成为了当下维护社会稳定和司法公正急切需要解决的新问题。本文将对打印照片的相机来源取证技术展开研究,主要工作总结如下：
(1)提出了一种基于模式噪声的打印扫描图像来源取证方法。
本文首先对传统的数字图像相机来源取证技术进行了研究,并对相机的成像系统以及在此过程中成像器件和处理操作对生成的数字图像产生的的影响进行了介绍。对于在数字图像来源取证中对不同相机来源的区分度较高的相机传感器模式噪声,分析了其作为特征进行打印扫描图像相机来源检测的可行性,通过比较选择出一种能够较大程度上滤除不含原始图像信息成分的滤波器用于提取噪声。最后,针对图像二次获取过程中可能引入的几何失真问题,提出了基于相机传感器模式噪声的打印扫描图像来源取证方法。实验结果表明,该方法对于打印扫描图像进行来源检测的平均准确率达到了76.8%,比Fridrich的方法提高了5%。而且该方法对于图像二次获取过程中引入的旋转、缩放、剪切等影响,均能达到稳定的检测效果。
(2)提出了一种基于高光成分去除的打印扫描图像来源取证方法。
通过对前面提出方法在实验中失效图像与有效图像的比较,发现扫描过程中引入的高光可能会对检测的结果产生不利影响。为了验证这一假设并能更加准确地检测图像来源,本文从高光产生的机理入手,利用双色反射模型和镜面反射-漫反射机制将图像中的镜面反射成分和漫反射成分进行了分离,从而达到了去除高光的目的。通过对四种品牌的相机进行的对比实验证明该方法能够将打印扫描图像的初始相机来源检测的平均准确率提高到84.3%。
As a kind of generated evidence, photo becomes more and more important in the judicial system and the criminal investigation. It usually has two forms, digital image and printed image, when delivered to court. Before the court receives the photo as evidence, its authenticity and source need to be identified. For this application background, scholars in the field of image forensics have been considered the digital image camera source identifying as an important research direction. But for printed photos, there are few findings come out.
In fact, we cannot obtain the original digital image in many cases. For example, provider only has the print version of the digital image with the original image deleted or the digital image delivered it is a recaptured image. Therefore, how to ensure the authenticity of these images and the reliability of the source becomes a new problem need to address for the urgent justice and social stability. In this paper, we will research the print-scanned image forensics technology, and main works are summarized as follows:
(1) Proposed a print-scanned image source identification method based on Pattern Noise.
In this paper, the traditional source of digital image forensics technology has been studied first. Then we introduced the imaging system of camera and the influences bring to the generated image by imaging device and processing operations in detail. Camera sensor Pattern Noise has higher differentiation of different cameras in digital image sources forensics. Through the comparison, we choose a filter which can ensure remaining the least information of original image to extract noise. At last, for the geometric distortion introduced by print-scan process, we proposed a print-scanned image source identification method based on Pattern Noise. Using this method, the average identification accuracy rate of test images reaches to 76.8%, which improves 5% compared with Fridrich's method. And the method can also reach the stable test results against the rotation, scale and cut introduced by the recapturing process.
(2) Proposed a source camera identification method of print-scanned digital image based on highlight removement.
Through the comparison and the analysis of the experiment before, we find that the failure images usually have visible highlight which could be the reason caused the lower result. In order to verify this hypothesis and solve the problems found in experiments, we analyze the generation of highlight. Using DR model and Specular-diffuse reflection, we separate specular reflection components and diffuse components of the image. Experiment results show that this is an effective method for source camera identification with less restrict, the average accuracy on distinguishing four cameras of print-scanned images improved to 84.3%
事实上在很多时候是无法获得原始数字图像的,例如照片的提供者只保留了数码照片的打印版本而删除了原始数字图像,或者提供的数字图像本身就是经过扫描获取到的等等。因此,如何能够确保这类图像来源的真实性和可靠性,也成为了当下维护社会稳定和司法公正急切需要解决的新问题。本文将对打印照片的相机来源取证技术展开研究,主要工作总结如下：
(1)提出了一种基于模式噪声的打印扫描图像来源取证方法。
本文首先对传统的数字图像相机来源取证技术进行了研究,并对相机的成像系统以及在此过程中成像器件和处理操作对生成的数字图像产生的的影响进行了介绍。对于在数字图像来源取证中对不同相机来源的区分度较高的相机传感器模式噪声,分析了其作为特征进行打印扫描图像相机来源检测的可行性,通过比较选择出一种能够较大程度上滤除不含原始图像信息成分的滤波器用于提取噪声。最后,针对图像二次获取过程中可能引入的几何失真问题,提出了基于相机传感器模式噪声的打印扫描图像来源取证方法。实验结果表明,该方法对于打印扫描图像进行来源检测的平均准确率达到了76.8%,比Fridrich的方法提高了5%。而且该方法对于图像二次获取过程中引入的旋转、缩放、剪切等影响,均能达到稳定的检测效果。
(2)提出了一种基于高光成分去除的打印扫描图像来源取证方法。
通过对前面提出方法在实验中失效图像与有效图像的比较,发现扫描过程中引入的高光可能会对检测的结果产生不利影响。为了验证这一假设并能更加准确地检测图像来源,本文从高光产生的机理入手,利用双色反射模型和镜面反射-漫反射机制将图像中的镜面反射成分和漫反射成分进行了分离,从而达到了去除高光的目的。通过对四种品牌的相机进行的对比实验证明该方法能够将打印扫描图像的初始相机来源检测的平均准确率提高到84.3%。
As a kind of generated evidence, photo becomes more and more important in the judicial system and the criminal investigation. It usually has two forms, digital image and printed image, when delivered to court. Before the court receives the photo as evidence, its authenticity and source need to be identified. For this application background, scholars in the field of image forensics have been considered the digital image camera source identifying as an important research direction. But for printed photos, there are few findings come out.
In fact, we cannot obtain the original digital image in many cases. For example, provider only has the print version of the digital image with the original image deleted or the digital image delivered it is a recaptured image. Therefore, how to ensure the authenticity of these images and the reliability of the source becomes a new problem need to address for the urgent justice and social stability. In this paper, we will research the print-scanned image forensics technology, and main works are summarized as follows:
(1) Proposed a print-scanned image source identification method based on Pattern Noise.
In this paper, the traditional source of digital image forensics technology has been studied first. Then we introduced the imaging system of camera and the influences bring to the generated image by imaging device and processing operations in detail. Camera sensor Pattern Noise has higher differentiation of different cameras in digital image sources forensics. Through the comparison, we choose a filter which can ensure remaining the least information of original image to extract noise. At last, for the geometric distortion introduced by print-scan process, we proposed a print-scanned image source identification method based on Pattern Noise. Using this method, the average identification accuracy rate of test images reaches to 76.8%, which improves 5% compared with Fridrich's method. And the method can also reach the stable test results against the rotation, scale and cut introduced by the recapturing process.
(2) Proposed a source camera identification method of print-scanned digital image based on highlight removement.
Through the comparison and the analysis of the experiment before, we find that the failure images usually have visible highlight which could be the reason caused the lower result. In order to verify this hypothesis and solve the problems found in experiments, we analyze the generation of highlight. Using DR model and Specular-diffuse reflection, we separate specular reflection components and diffuse components of the image. Experiment results show that this is an effective method for source camera identification with less restrict, the average accuracy on distinguishing four cameras of print-scanned images improved to 84.3%
引文
[1]摄影术_百度百科.http://baike.baidu.com/view/2676178.htm[EB/OL].
[2]http://www.xinhuwai.com/shuma/?action-viewnews-itemid-90274[EB/OL].
[3]张晖.基于背噪声盲估计的图像真伪鉴别[D].中国科学技术大学,2010.
[4]韩文文.基于亚像素的文本图像分割算法研究应用[D].华东师范大学,2008.
[5]郑君里.信号与系统[M].高等教育出版社,1978.
[6]http://www.pep.com.cn/gzwl/jszx/tbjx/kb/ck/xxll/201009/t20100903_869435.htm [EB/OL]
[7]数字图像_百度百科.http://baike.baidu.com/view/286845.htm
[8]翻拍or扫描?老照片在数字时代如何重生.http://tech.163.com/digi/10/0930/14/6HR8000T001624J3.html[EB/OL].
[9]拯救中国虎国际基金会.http://chinese.savechinastigers.org/[EB/OL].
[10]数码时代的摄影观念.http://qx818.cn/thread-32499-1-1.html [EB/OL].
[11]崔敏.刑事证据学[M].北京：中国人民公安大学山版社,1997.
[12]余荡峰.数字证据及其取证方法探究[J].中国人民公安大学学报：自然科学版,2004,1.
[13]陈源锋,陈群.数字技术与刑事证据—论数码照片的收集、审查和鉴定[J].福建公安高等专科学校学报—社会公共安全研究,2001,15(6)：44-47.
[14]周琳娜.数字图像盲取证技术研究[D].北京邮电大学,2007.
[15]杨义先,钮心忻.数字水印理论与技术[M].北京：高等教育出版社,2006.
[16]周琳娜,王东明.数字图像取证技术[M].北京：北京邮电大学出版社,2008.
[17]Friedman G. The trustworthy digital camera:Restoring credibility to the photographic image [J]. IEEE Transactions on Consumer Electronics,1993,39(4):905-910.
[18]吴琼.面向真实性检测的数字图像盲取证方法研究[D].国防科学技术大学,2008.
[19]胡永健,刘琲贝,贺前华.数字多媒体技术综述[J].计算机应用,2010,30(3)：657-662
[20]郭俐君.中国城市数码相机消费需求与市场竞争力报告出炉.中国工业报,2011,4(9).http://www.beehuu.com/digi/dzsm/2011-04-09/657.html [EB/OL].
[21]扫描仪_百度百科.http://baike.baidu.com/view/7818.htm [EB/OL].
[22]手机_百度百科.http://baike.baidu.com/view/1455.htm [EB/OL].
[23]施肖铮.现代数码摄影[J].常州信息职业技术学院学报,2003,4.
[24]刘远航,刘文开.数码相机原理简论[J].照相机,2002,7.
[25]侯雨石,陈永飞,何玉青等.数码相机原理与系统设计研究[J].光学技术,2002,5.
[26]于光勇.如何选购最适合你的数码相机[J].影像材料,2002,5.
[27]CCD_百度百科http://baike.baidu.com/view/18579.htm [EB/OL].
[28]CMOS传感器_百度百科.http://baike.baidu.com/view/356089.htm [EB/OL].
[29]E. Hecht. Optics [M]. San Francisco, California:Addison Wesly,2002.
[30]Pers J and Kovacic S. Nonparametric, model-based radial lens distortion correction using tilted camera assumption [C]. Proceedings of the Computer Vision Winter Workshop,2002:286-295.
[31]K. S. Choi, E. Y. Lam, K. Y. Wong. Automatic source camera identification using the intrinsic lens radial distortion [J]. Optics Express,2006,14(24):11551 11565.
[32]Geradts ZJ, Bijhold J, KieftM, et al. Methods for identification of images acquired with digital cameras [J].Proc. SPIE, Enabling technologies for law enforcement and security,2001,4232(1):505-12.
[33]徐之海,李齐.现代成像系统[M],北京：国防工业出版社,2001.
[34]马亚丁.白平衡、光圈及其调整刍议[J].西安文理学院学报：社会科学版,2006,4.
[35]A. C. Popescu, H. Farid. Exposing Digital Forgeries in Color Filter Array Interpolated Images [J]. IEEE Trans. on Signal Processing,2005, Vol.53:3948-3959.
[36]Hany Farid. Blind Inverse Gamma Correction [J]. IEEE Transactions on Image Processing, Oct,2001,10:1428-1433.
[37]S. Lyu, H. Farid. How realistic is photorealistic? [J]. IEEE Transactions on Signal Processing, February,2005,53(2):845-850.
[38]Ng. T. T., Chang S. F. Blind detection of photomontage using higher order statistics [C].Circuits and Systems,2004. vol.5:688-691.
[39]K. L. Bouman, N. Khanna, E. J. Delp. Digital Image Forensics Through the Use of Noise Reference Patterns [A]. Proceedings of the 2006 SURF Conference [C].2006.
[40]王波,孙璐璐,孔祥维,尤新刚.图像伪造中模糊操作的异常色调率取证技术[J].电子学报,2006,34(12)：2451-2454.
[41]沈林杰,孔祥维,尤新刚.基于字符图像质量评价的打印机取证[A]；第七届全国信息隐藏暨多媒体信息安全学术大会论文集[C],2007.
[42]吴玉宝,孔祥维,尤新刚.基于页面几何失真的打印机来源认证[J]；光电子.激光；2010,1.
[43]郭晓鸣,王波,孔祥维,周琳娜.相机模式噪声特征的数字图像来源认证方法.第九届全国信息隐藏暨多媒体信息安全学术大会(Proceedings of CIHW2010),2010,9:393-399.
[44]Xuehui Sun, Lisha Dong, Bo Wang, Xiangwei Kong, Xingang You. Source Cell-Phone Identification Based on Multi-feature Fusion [C]. IPCV,10-14th International Conference on Image processing, Computer Vision & pattern Recognition, July 12-15, 2010, USA.
[45]Lukas J, Fridrich J, Goljan M. Digital Camera Identification from Sensor Pattern Noise [J]. IEEE Transactions on Information Security and Forensics,2006,1(2): 205-214.
[46]Jan Lukas, Jessica Fridrich and Miroslav Goljan. Detecting Digital Image Forgeries Using Sensor Pattern Noise [J].In Proceedings of the SPIE, volume 6072,2006.
[47]Lukas J, Fridrich J, Goljan M. Determining Digital Image Origin Using Sensor Imperfections [C].Proc. SPIE. San Jose, California:Springer,2005:249-260.
[48]Lukas J, Fridrich J and Goljan M. Determining Digital Image Identification from Sensor Pattern Noise [J]. IEEE Transactions on Information Forensics and Security, 2005,1:205-214.
[49]Long Y J and Huang Y Z. Image based source camera identification using demosaicking [C]. In proceedings of 8th Workshop on Multimedia Signal Processing, Victoria, BC:IEEE,2006:419-424.
[50]S. Bayram, H. Sencar, N. Memon, et al. Source camera identification based on CFA interpolation [C]. IEEE International Conference on Image Processing, ICIP2005, Geneva, Italy, Sep.11-14,2005,3:Ⅲ-69-72.
[51]Swaminathan Ashwin, Wu Min, LiuKJRay. Image tampering identification using blind deconvolution [C].2006 IEEE International Conference on Image Processing, Atlanta, GA:IEEE,2006:2309-2312.
[52]Yu Feng Hsu, Shih-Fu Chang. Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency [C]. International Conference on Multimedia and Expo, Toronto, Canada,2006:549-552.
[53]Tian-Tsong Ng, Shih-Fu Chang, Mao-Pei Tsui. CRF Estimation using Single Channel Image Differential Invariants [EB/OL]. Columbia University,2006.
[54]CHEN M, FRIDRICH J, GOLIAN M. Digital imaging sensor identification (further study) [J].SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, San Jose, California,2007,6505:16-29.
[55]Mo Chen, Jessica Fridrich, Miroslav Goljan, Jan Lukas. Source Digital Camcorder Identification Using Sensor Photo Response Non-Uniformity [C].Proc. of SPIE Electronic Imaging, Photonics West,2007.
[56]GOLJAN M, FRIDRICH J, LUKAS J. Camera identification from printed images [C]. Proceedings of SPIE, Electronic Imaging, Forensics, Security, Steganography, and Watermarking of Multimedia Contents X. San Jose, CA:SPIE,2008:68190I.1 681901.12.
[57]G. C. Holst. CCD Arrays, Cameras, and Displays [M].2nd Edition. JCD Publishing & SPIE Press, USA,1998.
[58]Janesick, J. R. Scientific Charge-Coupled Devices [J]. SPIE PRESS Monograph, vol. PM83, SPIE-The International Society for Optical Engineering, January,2001.
[59]M. K. Mihcak, I. Kozintsev, K. Ramchandran. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising [C]. IEEE International Conference on Acoustics, Speech, and Signal Processing,1999,6: 3253-3256.
[60]Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, Karen Egiazarian. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing,2007,16:2080-2095.
[61]M. Chen, J. Fridrich, M. Goljan. Source Digital Camcorder Identification Using Sensor Photo Response Nonuniformity [J].Proc. SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, vol.6505, San Jose, California, January 28-February 1,2007.
[62]A.C. Popescu and H. Farid. Exposing Digital Forgeries by Detecting Traces of Resampling [J].IEEE Transactions on Signal Processing,2005,53 (2):758-767.
[63]O'Ruanaidh, J.J. K., Pun, T. Rotation, scale and translation invariant digital image watermarking [C]. Proceedings of International Conference on Image Processing,1997,1:536-539.
[64]GOLJAN M; FRIDRICH J. Camera identification from cropped and scaled images [J]. CA:SPIE,2008.
[65]B. V. K. Vijaya Kuma, L. Hassebrook. Performance Measures for Correlation Filters [J]. Appliod Optics,1990,29:2997-3006.
[66]蔡清源.三维物体表面的反射特性恢复[D].浙江大学信息科学与电子工程学,2010.
[67]S. Shafer. Using Color to Separate Reflection Components [J]. Color Research and Applications,1985,10:210-218.
[68]S. K. Nayar, X. S. Fang, T. Boult. Separation of reflection components using color and polarization [J]. International Journal of Computer Vision,1997,21 (3):163 186.
[69]R. T. Tan, K. Ikeuchi. Separating reflection components of textured surfaces using a single image [J].IEEE Trans. Pattern Analysis and Machine Intelligence,2005, 27(2):178-193.
[2]http://www.xinhuwai.com/shuma/?action-viewnews-itemid-90274[EB/OL].
[3]张晖.基于背噪声盲估计的图像真伪鉴别[D].中国科学技术大学,2010.
[4]韩文文.基于亚像素的文本图像分割算法研究应用[D].华东师范大学,2008.
[5]郑君里.信号与系统[M].高等教育出版社,1978.
[6]http://www.pep.com.cn/gzwl/jszx/tbjx/kb/ck/xxll/201009/t20100903_869435.htm [EB/OL]
[7]数字图像_百度百科.http://baike.baidu.com/view/286845.htm
[8]翻拍or扫描?老照片在数字时代如何重生.http://tech.163.com/digi/10/0930/14/6HR8000T001624J3.html[EB/OL].
[9]拯救中国虎国际基金会.http://chinese.savechinastigers.org/[EB/OL].
[10]数码时代的摄影观念.http://qx818.cn/thread-32499-1-1.html [EB/OL].
[11]崔敏.刑事证据学[M].北京：中国人民公安大学山版社,1997.
[12]余荡峰.数字证据及其取证方法探究[J].中国人民公安大学学报：自然科学版,2004,1.
[13]陈源锋,陈群.数字技术与刑事证据—论数码照片的收集、审查和鉴定[J].福建公安高等专科学校学报—社会公共安全研究,2001,15(6)：44-47.
[14]周琳娜.数字图像盲取证技术研究[D].北京邮电大学,2007.
[15]杨义先,钮心忻.数字水印理论与技术[M].北京：高等教育出版社,2006.
[16]周琳娜,王东明.数字图像取证技术[M].北京：北京邮电大学出版社,2008.
[17]Friedman G. The trustworthy digital camera:Restoring credibility to the photographic image [J]. IEEE Transactions on Consumer Electronics,1993,39(4):905-910.
[18]吴琼.面向真实性检测的数字图像盲取证方法研究[D].国防科学技术大学,2008.
[19]胡永健,刘琲贝,贺前华.数字多媒体技术综述[J].计算机应用,2010,30(3)：657-662
[20]郭俐君.中国城市数码相机消费需求与市场竞争力报告出炉.中国工业报,2011,4(9).http://www.beehuu.com/digi/dzsm/2011-04-09/657.html [EB/OL].
[21]扫描仪_百度百科.http://baike.baidu.com/view/7818.htm [EB/OL].
[22]手机_百度百科.http://baike.baidu.com/view/1455.htm [EB/OL].
[23]施肖铮.现代数码摄影[J].常州信息职业技术学院学报,2003,4.
[24]刘远航,刘文开.数码相机原理简论[J].照相机,2002,7.
[25]侯雨石,陈永飞,何玉青等.数码相机原理与系统设计研究[J].光学技术,2002,5.
[26]于光勇.如何选购最适合你的数码相机[J].影像材料,2002,5.
[27]CCD_百度百科http://baike.baidu.com/view/18579.htm [EB/OL].
[28]CMOS传感器_百度百科.http://baike.baidu.com/view/356089.htm [EB/OL].
[29]E. Hecht. Optics [M]. San Francisco, California:Addison Wesly,2002.
[30]Pers J and Kovacic S. Nonparametric, model-based radial lens distortion correction using tilted camera assumption [C]. Proceedings of the Computer Vision Winter Workshop,2002:286-295.
[31]K. S. Choi, E. Y. Lam, K. Y. Wong. Automatic source camera identification using the intrinsic lens radial distortion [J]. Optics Express,2006,14(24):11551 11565.
[32]Geradts ZJ, Bijhold J, KieftM, et al. Methods for identification of images acquired with digital cameras [J].Proc. SPIE, Enabling technologies for law enforcement and security,2001,4232(1):505-12.
[33]徐之海,李齐.现代成像系统[M],北京：国防工业出版社,2001.
[34]马亚丁.白平衡、光圈及其调整刍议[J].西安文理学院学报：社会科学版,2006,4.
[35]A. C. Popescu, H. Farid. Exposing Digital Forgeries in Color Filter Array Interpolated Images [J]. IEEE Trans. on Signal Processing,2005, Vol.53:3948-3959.
[36]Hany Farid. Blind Inverse Gamma Correction [J]. IEEE Transactions on Image Processing, Oct,2001,10:1428-1433.
[37]S. Lyu, H. Farid. How realistic is photorealistic? [J]. IEEE Transactions on Signal Processing, February,2005,53(2):845-850.
[38]Ng. T. T., Chang S. F. Blind detection of photomontage using higher order statistics [C].Circuits and Systems,2004. vol.5:688-691.
[39]K. L. Bouman, N. Khanna, E. J. Delp. Digital Image Forensics Through the Use of Noise Reference Patterns [A]. Proceedings of the 2006 SURF Conference [C].2006.
[40]王波,孙璐璐,孔祥维,尤新刚.图像伪造中模糊操作的异常色调率取证技术[J].电子学报,2006,34(12)：2451-2454.
[41]沈林杰,孔祥维,尤新刚.基于字符图像质量评价的打印机取证[A]；第七届全国信息隐藏暨多媒体信息安全学术大会论文集[C],2007.
[42]吴玉宝,孔祥维,尤新刚.基于页面几何失真的打印机来源认证[J]；光电子.激光；2010,1.
[43]郭晓鸣,王波,孔祥维,周琳娜.相机模式噪声特征的数字图像来源认证方法.第九届全国信息隐藏暨多媒体信息安全学术大会(Proceedings of CIHW2010),2010,9:393-399.
[44]Xuehui Sun, Lisha Dong, Bo Wang, Xiangwei Kong, Xingang You. Source Cell-Phone Identification Based on Multi-feature Fusion [C]. IPCV,10-14th International Conference on Image processing, Computer Vision & pattern Recognition, July 12-15, 2010, USA.
[45]Lukas J, Fridrich J, Goljan M. Digital Camera Identification from Sensor Pattern Noise [J]. IEEE Transactions on Information Security and Forensics,2006,1(2): 205-214.
[46]Jan Lukas, Jessica Fridrich and Miroslav Goljan. Detecting Digital Image Forgeries Using Sensor Pattern Noise [J].In Proceedings of the SPIE, volume 6072,2006.
[47]Lukas J, Fridrich J, Goljan M. Determining Digital Image Origin Using Sensor Imperfections [C].Proc. SPIE. San Jose, California:Springer,2005:249-260.
[48]Lukas J, Fridrich J and Goljan M. Determining Digital Image Identification from Sensor Pattern Noise [J]. IEEE Transactions on Information Forensics and Security, 2005,1:205-214.
[49]Long Y J and Huang Y Z. Image based source camera identification using demosaicking [C]. In proceedings of 8th Workshop on Multimedia Signal Processing, Victoria, BC:IEEE,2006:419-424.
[50]S. Bayram, H. Sencar, N. Memon, et al. Source camera identification based on CFA interpolation [C]. IEEE International Conference on Image Processing, ICIP2005, Geneva, Italy, Sep.11-14,2005,3:Ⅲ-69-72.
[51]Swaminathan Ashwin, Wu Min, LiuKJRay. Image tampering identification using blind deconvolution [C].2006 IEEE International Conference on Image Processing, Atlanta, GA:IEEE,2006:2309-2312.
[52]Yu Feng Hsu, Shih-Fu Chang. Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency [C]. International Conference on Multimedia and Expo, Toronto, Canada,2006:549-552.
[53]Tian-Tsong Ng, Shih-Fu Chang, Mao-Pei Tsui. CRF Estimation using Single Channel Image Differential Invariants [EB/OL]. Columbia University,2006.
[54]CHEN M, FRIDRICH J, GOLIAN M. Digital imaging sensor identification (further study) [J].SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, San Jose, California,2007,6505:16-29.
[55]Mo Chen, Jessica Fridrich, Miroslav Goljan, Jan Lukas. Source Digital Camcorder Identification Using Sensor Photo Response Non-Uniformity [C].Proc. of SPIE Electronic Imaging, Photonics West,2007.
[56]GOLJAN M, FRIDRICH J, LUKAS J. Camera identification from printed images [C]. Proceedings of SPIE, Electronic Imaging, Forensics, Security, Steganography, and Watermarking of Multimedia Contents X. San Jose, CA:SPIE,2008:68190I.1 681901.12.
[57]G. C. Holst. CCD Arrays, Cameras, and Displays [M].2nd Edition. JCD Publishing & SPIE Press, USA,1998.
[58]Janesick, J. R. Scientific Charge-Coupled Devices [J]. SPIE PRESS Monograph, vol. PM83, SPIE-The International Society for Optical Engineering, January,2001.
[59]M. K. Mihcak, I. Kozintsev, K. Ramchandran. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising [C]. IEEE International Conference on Acoustics, Speech, and Signal Processing,1999,6: 3253-3256.
[60]Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, Karen Egiazarian. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing,2007,16:2080-2095.
[61]M. Chen, J. Fridrich, M. Goljan. Source Digital Camcorder Identification Using Sensor Photo Response Nonuniformity [J].Proc. SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, vol.6505, San Jose, California, January 28-February 1,2007.
[62]A.C. Popescu and H. Farid. Exposing Digital Forgeries by Detecting Traces of Resampling [J].IEEE Transactions on Signal Processing,2005,53 (2):758-767.
[63]O'Ruanaidh, J.J. K., Pun, T. Rotation, scale and translation invariant digital image watermarking [C]. Proceedings of International Conference on Image Processing,1997,1:536-539.
[64]GOLJAN M; FRIDRICH J. Camera identification from cropped and scaled images [J]. CA:SPIE,2008.
[65]B. V. K. Vijaya Kuma, L. Hassebrook. Performance Measures for Correlation Filters [J]. Appliod Optics,1990,29:2997-3006.
[66]蔡清源.三维物体表面的反射特性恢复[D].浙江大学信息科学与电子工程学,2010.
[67]S. Shafer. Using Color to Separate Reflection Components [J]. Color Research and Applications,1985,10:210-218.
[68]S. K. Nayar, X. S. Fang, T. Boult. Separation of reflection components using color and polarization [J]. International Journal of Computer Vision,1997,21 (3):163 186.
[69]R. T. Tan, K. Ikeuchi. Separating reflection components of textured surfaces using a single image [J].IEEE Trans. Pattern Analysis and Machine Intelligence,2005, 27(2):178-193.