图像内容被动取证技术的研究
|
摘要
随着计算机技术的发展,作为其分支之一的数字图像处理技术的发展也是日新月异,新的数字图像处理技术和相应的软件不断地涌现,其功能变得越来越强大而操作同时变得越来越“傻瓜”。越来越多的非专业摄影人士也能熟练使用图像处理软件。以前需要在暗房中经过繁琐的操作才能完成一幅图像的篡改操作,而现在只需要一台普通的计算机和功能强大的数字图像处理软件就可以让一个普通的非专业人士完成一幅图像的篡改。一个熟练的操作者能让图像不留下肉眼能察觉的痕迹,达到以假乱真的目的。对图像真伪的判断光靠肉眼是看不出任何端倪的。因此图像处理产生了一种新的技术——数字图像被动取证技术(也称盲取证技术)。数字图像被动取证技术就是利用各种检测手段对图像进行检测和研究,从而判断出图像是否被篡改、是否来自同一获取设备或者是否计算机生成等。
图像被动取证技术与图像主动取证技术最大的区别在于:图像主动取证技术需要事先往图像中添加数字水印或者数字签名信息,然后通过检测数字水印或数字签名的完整性来判断图像是否受到了篡改;而被动取证不需要事先添加任何信息到图像中,而是直接利用图像自身信息来进行鉴别和取证,因而被动取证具有更广阔的应用前景。
由于图像被动取证涉及范围很广,如针对图像真伪,针对图像来源,针对图像是否计算机生成等应用领域,不可能一应俱全都研究到。本文针对图像内容被动取证进行了较深入的研究,主要研究内容和取得的研究成果如下:
1.针对现阶段图像被动取证技术没有一个公认的确定的理论框架结构这个问题,在查阅大量的参考文献基础上,结合开闭环控制思想,初步构建了一种图像被动取证的框架;在该框架中首先利用标准图像数据库和质量评价指标不断调整优化特征提取方法,使取证算法所使用的特征量能更准确的刻画图像篡改部分的本质,同时也对图像取证算法进行不断优化,得到优化后的特征提取和优化后的取证算法;再利用优化后的特征量和优化了的取证算法对未知篡改类型的图像进行被动取证;
2.针对现阶段的被动取证技术手段都比较单一,尚没有一个确切的分类方法,本文提出了一种被动取证技术的分类方法,将其分为:基于图像内容的被动取证、基于成像过程的被动取证和基于物理原理的被动取证;
3.图像篡改中为了掩饰篡改痕迹往往会对图像中物体的边缘进行模糊操作,本文对此展开研究,将模糊数学和粗糙集理论引入其中,提出了一种基于模糊增强和粗糙集的新算法,结果表明该算法能较好的检测出被人工模糊操作的边缘;
4.将水平投影和垂直投影引入到图像被动取证中,提出了基于水平投影和垂直投影的图像被动取证方法。与Posucue算法相比表明了,新算法具有更快的速度;还引进了改进后的圆投影,试验结果证明,利用改进的圆投影可以克服水平垂直投影不能抗旋转的缺点,对角度旋转,光照变化都具有一定的鲁棒性;
5.首次将仿射尺度不变特征变换算法(ASIFT)用于图形复制--粘贴型篡改中,提出了ASIFT的图像复制—粘贴型篡改取证方法,试验结果表明,该算法在图像深度旋转,大尺度变化时有较好的表现;首次将流形算法中的局部线性嵌入算法(LLE)用于复制—粘贴型篡改检测中,结果表明,跟用主元分析法降维方法相比,基于LLE的算法对图像的模糊操作具有一定的鲁棒性,同时在原有的LLE算法上进行了改进,使得LLE处理速度得到一定程度的提高。
最后在总结本文研究的基础上,对图像被动取证的未来发展方向进行了展望。
With the development of the computer technology, digital image processing technology, as one branch of computer science, is developing fast. New technology and new software of image processing have come forth with more convenient manipulation. More and more people can modify the pictures only with a computer and image processing software such as Photoshop and 3DMAX. A modified picture can not be recognized by eyes because of lacking of the tampering traces. Passive image forensics has been developed which detects the image by many methods to find out the fact whether the picture is modified or not. Meanwhile, it can also tell us that the picture is natural image or a computer generate.
The most difference between passive image forensics and active one is that digital signature and digital watermark should be embedded in images in advanced in active forensics. The passive forensics uses the information which is applied by the indeed images. Then, the passive one will have more applications.
This paper reveals the research on passive image forensics based on image content. The main achievements reveal as following:
Aiming at the question that there is no acknowledged theoretical framework of passive forensics, it gives a preliminary framework.
Aiming at the problems that every method revealed is detecting one certain tamper, there is no one certain classification method. Then it classified all methods into three classifications which are passive forensics based on image content, passive forensics based on the capturing process and passive forensics based on physical principles.
Aiming at edge flurring, the general manipulation in image tampering, a new algorithm has been applied which uses fuzzy enhancement and rough sets to find out the edges which is flurred manually. The result shows the algorithm works well.
Applying projection data to passive image forensics, a new algorithm is more rapid than Posucue’s algorithm. Furthermore, it improves the algorithm by replacing horizontal/vertical projection to Ring projection which makes the algorithm rotation invariant and robust to illumination.
ASIFT algorithm has been applied in detection the copy-paste forgery at the first time. The result shows that the algorithm can work well when the image rotates and large scale changes. LLE algorithm has been applied in detection the copy-paste forgery at the first time. LLE is some robust to edge flurred. An improved LLE shows that the improved one is more rapid than original LLE.
Finally, the paper summarized the research works and gave the prospect of passive image forensics in future.
图像被动取证技术与图像主动取证技术最大的区别在于:图像主动取证技术需要事先往图像中添加数字水印或者数字签名信息,然后通过检测数字水印或数字签名的完整性来判断图像是否受到了篡改;而被动取证不需要事先添加任何信息到图像中,而是直接利用图像自身信息来进行鉴别和取证,因而被动取证具有更广阔的应用前景。
由于图像被动取证涉及范围很广,如针对图像真伪,针对图像来源,针对图像是否计算机生成等应用领域,不可能一应俱全都研究到。本文针对图像内容被动取证进行了较深入的研究,主要研究内容和取得的研究成果如下:
1.针对现阶段图像被动取证技术没有一个公认的确定的理论框架结构这个问题,在查阅大量的参考文献基础上,结合开闭环控制思想,初步构建了一种图像被动取证的框架;在该框架中首先利用标准图像数据库和质量评价指标不断调整优化特征提取方法,使取证算法所使用的特征量能更准确的刻画图像篡改部分的本质,同时也对图像取证算法进行不断优化,得到优化后的特征提取和优化后的取证算法;再利用优化后的特征量和优化了的取证算法对未知篡改类型的图像进行被动取证;
2.针对现阶段的被动取证技术手段都比较单一,尚没有一个确切的分类方法,本文提出了一种被动取证技术的分类方法,将其分为:基于图像内容的被动取证、基于成像过程的被动取证和基于物理原理的被动取证;
3.图像篡改中为了掩饰篡改痕迹往往会对图像中物体的边缘进行模糊操作,本文对此展开研究,将模糊数学和粗糙集理论引入其中,提出了一种基于模糊增强和粗糙集的新算法,结果表明该算法能较好的检测出被人工模糊操作的边缘;
4.将水平投影和垂直投影引入到图像被动取证中,提出了基于水平投影和垂直投影的图像被动取证方法。与Posucue算法相比表明了,新算法具有更快的速度;还引进了改进后的圆投影,试验结果证明,利用改进的圆投影可以克服水平垂直投影不能抗旋转的缺点,对角度旋转,光照变化都具有一定的鲁棒性;
5.首次将仿射尺度不变特征变换算法(ASIFT)用于图形复制--粘贴型篡改中,提出了ASIFT的图像复制—粘贴型篡改取证方法,试验结果表明,该算法在图像深度旋转,大尺度变化时有较好的表现;首次将流形算法中的局部线性嵌入算法(LLE)用于复制—粘贴型篡改检测中,结果表明,跟用主元分析法降维方法相比,基于LLE的算法对图像的模糊操作具有一定的鲁棒性,同时在原有的LLE算法上进行了改进,使得LLE处理速度得到一定程度的提高。
最后在总结本文研究的基础上,对图像被动取证的未来发展方向进行了展望。
With the development of the computer technology, digital image processing technology, as one branch of computer science, is developing fast. New technology and new software of image processing have come forth with more convenient manipulation. More and more people can modify the pictures only with a computer and image processing software such as Photoshop and 3DMAX. A modified picture can not be recognized by eyes because of lacking of the tampering traces. Passive image forensics has been developed which detects the image by many methods to find out the fact whether the picture is modified or not. Meanwhile, it can also tell us that the picture is natural image or a computer generate.
The most difference between passive image forensics and active one is that digital signature and digital watermark should be embedded in images in advanced in active forensics. The passive forensics uses the information which is applied by the indeed images. Then, the passive one will have more applications.
This paper reveals the research on passive image forensics based on image content. The main achievements reveal as following:
Aiming at the question that there is no acknowledged theoretical framework of passive forensics, it gives a preliminary framework.
Aiming at the problems that every method revealed is detecting one certain tamper, there is no one certain classification method. Then it classified all methods into three classifications which are passive forensics based on image content, passive forensics based on the capturing process and passive forensics based on physical principles.
Aiming at edge flurring, the general manipulation in image tampering, a new algorithm has been applied which uses fuzzy enhancement and rough sets to find out the edges which is flurred manually. The result shows the algorithm works well.
Applying projection data to passive image forensics, a new algorithm is more rapid than Posucue’s algorithm. Furthermore, it improves the algorithm by replacing horizontal/vertical projection to Ring projection which makes the algorithm rotation invariant and robust to illumination.
ASIFT algorithm has been applied in detection the copy-paste forgery at the first time. The result shows that the algorithm can work well when the image rotates and large scale changes. LLE algorithm has been applied in detection the copy-paste forgery at the first time. LLE is some robust to edge flurred. An improved LLE shows that the improved one is more rapid than original LLE.
Finally, the paper summarized the research works and gave the prospect of passive image forensics in future.
引文
[1]李丽新,袁烨.网络安全通讯中的数字签名[J].现代情报,2007,8:53-56
[2]赵泽茂.数字签名理论[M].北京:科学出版社,2007:1-80
[3]Mohan Atreya.数字签名[M].北京:清华大学出版社,2003:1-50
[4]吴晓云.浅谈数字图像的主动取证技术[J].信息技术,2008, (18):11-12
[5]孙圣和,陆哲明,牛夏牧.数字水印技术及应用[M].北京:科学出版社,2004:10-150
[6]吴金海,林福宗.基于数字水印的图像认证技术[J].计算机学报,2004,27(9):1153-1160
[7]Hany Farid.Creating and Detecting Doctored and Virtual Images Implications to The Child Pornography Prevention Act [OL].TR2004-518, Dartmouth College, Computer Science
[8]Hany Farid.Image Forgery Detection[M].IEEE Signal Processing Magazine, 2009,(3):16-25
[9]Hany Farid.Exposing Digital Forgeries by Detecting Duplicated Image Regions[OL].Tr2004-515, DARTMUTH College, Computer Science
[10]Micah K . Johnson,Hany Farid . Exposing Digital Forgeries Through Chromatic Aberration[C].Proceedings of the Multimedia and Security Workshop 2006,v2006:48-55
[11]Eric Kee,Hany Farid.Printer Profiling for Forensics and Ballistics[C].Proceedings of the 10th ACM Workshop on Multimedia and Security,2008:3-9
[12]Micah K.Johnson,Hany Farid.Detecting Photographic Composites of People[C].Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008,5041:19-33
[13]Hany Farid.Blind Inverse Gamma Correction[J].IEEE Transactions ON Image Processing, 2001,10(10):1428-1433
[14]Johnson Micah K,HanyFarid.Exposing digital forgeries by detecting inconsistencies in lighting Source[C] .Proceedings of the 7th Multimedia and Security Workshop, 2005(2006):1-9
[15]Alin C. Popescu,Hany Farid.Exposing Digital Forgeries by Detecting Traces of sampling[J]. IEEE Transactions ON Signal Processing, 2005,53(2):758-767
[16] Alin C. Popescu, Hany Farid.Exposing Digital Forgeries in Color Filter Array Interpolated Images[J].IEEE Transactions ON Signal Processing, 2005,53(10):3948-3959
[17]Johnson Micah K, Farid, Hany.Exposing digital forgeries through specular highlights on the eye[C] .Information Hiding - 9th International Workshop, IH 2007, Revised Selected Papers,LectureNotes in Computer Science, 2007, 4567: 311-325
[18] Siwei Lyu.Natural Image Statistics for Digital Image Forensics[OL] .http://pqdt.calis.edu.cn/
[19] Tian-Tsong Ng, Shih-Fit Chang.A Model for image splicing[C] .Proceedings - International Conference on Image Processing, ICIP, 2004, 4:1169-1172
[20]Tian-Tsong Ng, Shih-Fu Chang, Sun Qibin.Blind detection of photomontage using higher order statistics[C].IEEE International Symposium on Circuits and Systems, 2004, 5:V688-V691
[21]Hsu Yu-Feng, Chang, Shih-Fu.Detecting image splicing using geometry invariants and camera characteristics consistency[C].IEEE International Conference on Multimedia and Expo,2006, 2006:549-552
[22] Tian-Tsong Ng, Shih-Fu Chang,Mao-Pei Tsui.Lessons Learned from Online Classification of Photo-Realistic Computer Graphics and Photographs[C] .Signal Processing Applications for Public Security and Forensics, 2007
[23]Tian-Tsong Ng, Shih-Fu Chang.Camera Response Function Estimation from a Single-channel Image Using Differential Invariants[OL].http://www.ee.columbia.edu/ln/dvmm/publicationPage//Author//Shih-Fu.Chang.html
[24]Shwin Swaminathan, Min Wu, K. J. Ray Liu.A Pattern Classification Framework for Theoretical Analysis of component forensics[C] .IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2008:1665-1668
[25]Swaminathan, Ashwin,Wu, Min,Liu, K.J. Ray.Component forensics of digital cameras: A non-intrusive approach[C].IEEE Conference on Information Sciences and Systems, 2006:1194-1199
[26]Christine McKay, Ashwin Swaminathan, Hongmei Gou.Image Acquisition Forensics:Forensic Analysis To Identify Imaging Source[C] .IEEE International Conference on Acoustics, Speech and Signal Processing– Proceedings, 2008:1657-1660
[27] Hongmei Gou, Ashwin Swaminathan, Min Wu.Noise Features For Image Tampering Detection And Steganalysis[C] .Proceedings - International Conference on Image Processing, 2006, 6: VI97-VI100
[28] Ashwin Swaminathan, Min Wu, K. J. Ray Liu.Digital Image Forensics via Intrinsic Fingerprints[J].IEEE Transactions on Information Forensics and Security, 2008,3(1):101-117
[29]Ashwin Swaminathan, Min Wu, K. J. Ray Liu.Intrinsic Fingerprints for Image Authentication and Steganalysis[C] .Proceedings of SPIE - The International Society for Optical Engineering,2007: v 6505
[30]Ashwin Swaminathan, Min Wu, K. J. Ray Liu.A Component Estimation Framework for InformationForensics Multimedia Signal Processing[C].IEEE 9th Workshop on Digital Object Identifier, 2007:397– 400
[31] Hongmei Gou, Ashwin Swaminathan, Min Wu.Noise Features for Image Tampering Detection and Steganalysis[C].IEEE International Conference on Image Processing, 2007, 6:VI 97-VI100
[32]Swaminathan, A, Min Wu, Liu, K.J.R.Non-Intrusive Forensic Analysis of Visual Sensors Using Output Images[C] .IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2006:V401-V404
[33] Ashwin Swaminathan, Min Wu and K. J. Ray Liu.Optimization of input pattern for semi non-intrusive component forensics of digital cameras[C] .IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2007, 2:II225-II228
[34]Hongmei Gou, Ashwin Swaminathan, Min Wu.Robust scanner identification based on noise features.[C].Proceedings of SPIE - The International Society for Optical Engineering, 2007:6505,
[35]Jan Luká?, Jessica Fridrich,Miroslav Goljan.Detecting Digital Image Forgeries Using Sensor Pattern Noise[C].Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 6072, 60720
[36]Jessica Fridrich, David Soukal,Jan Lukás.Detection of Copy-Move Forgery in Digital Images[OL] .http://www.ws.binghamton.edu/fridrich/publications.html
[37]Jan Luká?, Jessica Fridrich.Estimation of primary quantization matrix for steganalysis of double-compressed JPEG images[C] .Proceedings of SPIE - The International Society for Optical Engineering, 2008: v6819
[38]Ismail Avcibas, Sevinc Bayram, Nasir Memon.A classifier Design For Detecting Image Manipulations[C].Proceedings - International Conference on Image Processing,2004,4:2645-2648
[39]Bayram Sevinc,Sencar Husrev Taha, Memon Nasir.An efficient and robust method for detecting copy-move forgery[C] .IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2009:1053-1056
[40]Y. Sutcu, B. Coskun. H.T. Sencar,N Memon.Tamper detection based on regularity of wavelet transform coefficients[C] . International Conference on Image Processing, 2007,1:I397-I400
[41]Sevin? Bayram,Smail Avc?bas,Bülent Sankur.Image manipulation detection[J] .Journal of Electronic Imaging ,2006,15(4):041102-01—041102-17
[42]Yun Q. Shi,Chunhua Chen,Wen Chen.A Natural Image Model Approach to Splicing Detection[C] .Proceedings of the Multimedia and Security Workshop, 2007: 51-62
[43]孙堡垒,周琳娜,张茹.基于Benford的高斯模糊篡改取证[J].计算机研究与发展,2009, 6(Suppl.):211—216
[44]周琳娜,王东明,郭云彪,杨义先.基于数字图像边缘特性的形态学滤波取证技术[J].电子学报, 2008,36(6):1047-1051
[45]吴琼,李国辉,孙韶杰,涂丹.基于小波和奇异值分解的图像复制伪造区域检测[J].小型微型计算机系统,2008,29(4):730-733
[46]吴琼.数字图像盲取证对样本合成修复应用的篡改区域检测算法[J].自动化学报,2009,35(5):239-243
[47]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(12A):2451-2454
[48]朱秀明,宣国荣,姚秋明.信息取证中图像重采样检测[J].计算机应用,2006,26(11):2596-2601
[49]骆伟祺,黄继武,丘国平.鲁棒的区域复制图像篡改检测技术[J].计算机学报, 2007,30(11):1998-2007
[50]王俊文,刘光杰,张湛.图像区域复制篡改快速鲁棒取证[J].自动化学报,2009,35(12):1488-1495
[51]王玉平,李生红,赵峰.基于AdaBoost的计算机生成图像检测算法[J] .计算机仿真,2008,25(7):220-222
[52]陈英,赵鹏,王瑀屏.基于边界检测的虚假图像盲识别算法[J].软件学报,2008, 19(12sup):59?68
[53]Tian-Tsong Ng,Shih-Fu Chang, Ching-Yung Lin.Passive-blind Image Forensics[J].Multimedia Security technologies for digital rights, Elsevier,2006:1-30
[54]吴琼.面向真实性检测的数字图像盲取证方法研究[D].长沙:国防科技大学,2008
[55]PIM and PRCG图像数据库.PIM and PRCG图像数据库[OL].http://www.ee.columbia.edu/trustfoto
[56]蒋刚毅,黄大江,王旭.图像质量评价方法研究进展[J].电子与信息学报,2010,32(1):219-225
[57]Dun-Yu Hsiao,Soo-Chang Pei.Detecting digital tampering by blur estimation[C] .Sadfe’05, Taipei, Taiwan, 2005:264-278
[58]Y.Sutcu, B.Cosk.Tamper detection based on regularity of wavelet transform coefficients[C].ICIP2007, San Antonio, Texas, 2007:397-400
[59]AlinC.Popescu,HanyFarid.Statisticaltoolsfordigitalforensics[OL].http://www.cs.dartmouth.edu/farid/research/
[60]He J F,Lin Z C,Wang L F . Detecting doctored JPEG images via DCT coefficient analysis[C].Proceedings of 9th European Conference on Computer Vision, Graz, Austria, 2006:423-435
[61]YE S, SUN Q, CHANG E C.Detecting digital image forgeries by measuring inconsistencies of blocking artifacts[C] .In: Proceedings of 2007 IEEE International Conference on Multimedia and Expo Washington, DC,IEEE Computer Society, 2007:12-15
[62]Micah K. J Johnson, Hany Farid.Exposing digital forgeries in complex lighting environments[J].IEEE Transactions on Information Forensics and Security, 2007,2(3):450-461
[63]Oya Celiktutan, Ismail Avcibas,Bulent Sankur.Blind identification of Cellular Phone Cameras[C].SPIE 2007, San Jose, CA, USA, 2007:1-4
[64]王渝霞.试论静态图像的原始性检验[J].四川警官高等专科学校学报,2006,18(1):68-71
[65]老编.质疑必须以认真的科学态度来分析解读[OL] .http://www.xitek.com/
[66]L.A.Zadeh.Fuzzy Sets[J].Information and Control,1965,8(3):338-353
[67]郭桂蓉.模糊模式识别[M].长沙:国防科技大学出版社,1992:134-159.
[68]Pal.S.k,King.R.A.Image Enhancement using Smoothing width Fuzzy Set[J].IEEE Trans,Syst,Man,Cybern,1981,SMC-11,7:494-501.
[69]Z Pawlak.Rough Sets[J].International Journal of Computer and Information Sciences.2000,1882(11):341-356
[70]徐立中.数字图像的智能信息处理[M].2版.长沙:国防工业出版社,2007:42-63
[71]褚健,荣冈.流程工业综合自动化技术[M].北京:机械工业出版社,2004:157-159
[72]庄天戈.CT原理与算法[M].上海:上海交通大学出版社,1992:6—50
[73]鲁珂,赵继东,叶娅兰.保局投影算法在图像检索中的应用研究[J].计算机应用研究,2006(12):56-58
[74]孙强,张维娜,姚弘奕.基于Radon方向投影特征的文物真伪鉴别方法[J].吉林工业大学自然科学学报,2001,31(2):82-85
[75]马惠敏.基于投影的灰度图像特征提取[M].光学技术,2002,28(6):561-562
[76]桂志国,薄瑞峰,韩炎.基于投影特征的图像匹配的快速算法[J]华北工学院测试技术学报. Vol.14, No.1,2000:18-20
[77]周治紧,李玉鑑.基于投影归一化的字符特征提取方法[J].计算机工程,2006,32(2):197-199
[78]赵振勇,王保华,王力.人脸图像的特征提取[J].计算机技术与发展[J],2007,17(5):221-224
[79]郭瑞,张淑玲,汪小芬.人脸识别特征提取方法和相似度匹配方法研究[J].计算机工程,2006,32(11):225-227
[80]祝磊,朱善安.人脸识别的一种新的特征提取方法[J].光电工程,2007,34(6):122-125
[81]全书海,薛志华,王琴.基于投影图像分布特征的车牌定位算法研究与实现[J].武汉理工大学学报,2005,29(6):879-882
[82]赵春明,石跃祥.利用投影特征高速识别车牌中的汉字[J].计算机工程与应用,2005(19):207-209
[83]刘宁钟,杨静宇.基于投影算法的二维条码识别[J].计算机工程,2002,28(9):32-33
[84]王敬东,徐亦斌,沈春林.一种新的任意角度旋转的景象匹配方法[J].南京航空航天大学学报,2005,37(1):6-10
[85]Lin Y H,Chen C H,Wei C C.New method for subpixel image matching with rotation invariance by combining the parametric template method and the ring projection transform process[J].Optical Engineering, 2006,45(6)
[86]徐亦斌,王敬东,李鹏.基于圆投影向量的景象匹配方法研究[J].系统工程与电子技术,.2005,27(10):1725-1728
[87]王姮,刘冉,解兴哲.新型图像角度匹配方法及其应用研究[J].计算机工程与应用,2007,47(4):208-211
[88]M. S. CHOI.A novel two stage template matching method for rotation and illumination invariance [J].Pattern Recongnition,2002,35:119-129
[89]Lisa Gottesfeld Brown.A Survey of Image Registration Techniques[J].ACM Computing Sureveys, 1992,24(4)
[90]J.P.Lewis.Fast Normalized Cross-Correlation[OL].http://www.docin.com/p-65883159.html
[91]D.I. Barnea, H.F.Silverman.A class of algorithms for fast digital image registration[J].IEEE Transactions on Computing,1972,21:179-186
[92]杨占龙.基于特征点的图像配准与拼接技术研究[D].西安:西安电子科技,2008
[93]Harris C,Stephens.A combined coner and edge detector[C] .Roc.4the Alvey Vision Conf, 1988:147-151
[94]PRESS W H.The art of scientific computiong[M].Cambridge: Cambridge University Press, 1986:498-546
[95]Mikolajczyk, K, Schmid, C.Indexing based on scale invariant interest points[C] .Proceedings of the IEEE International Conference on Computer Vision, 2001,1:525-531
[96]Mikolajczyk, K. Tuytelaars T., Schmid C. A comparison of affine region detectors[J]. International Journal of Computer Vision, v 65, n 1-2, p 43-72, November 2005
[97]杨健.基于Harris-Affine特征的图像检索系统研究与实现[D].大连:大连理工大学,2008
[98]D.G. Lowe.Distinctive image features from scale invariant keypoints[J].International Journal of Computer Vision, 2004,60(2):91-110
[99]Lindeberg T. Scale-space for discrete signal[J].IEEE Transaction PAMI,1980,207:187-207
[100]Guoshen Yu.Jean-Michil Morel.A fully affine invariant image comparison method[C] .The 34th International Conference on Acoustics, Speech, and Signal Processing, 2009:1597-1600
[101]Jean-Michil Morel,Guoshen Yu.ASIFT:A New Framework For Fully Affine Invariant Image Comparison[OL].http://www.cmap.polytechnique.fr/~yu/research/ASIFT/demo.html
[102]Sebastian HS,Lee DD.The manifold ways of perception[J].Science,2000,290(12):2268-2269
[103]Roweis ST, Saul L K.Nonlinear dimensionality analysis by locally linear embedding[J].Science, 2000, 290(12):2319-2323
[104]Tenenbaum JB,de Silva V, Langford JC.A global geometric framework for nonlinear dimensionality reduction[J].Scinece, 2000, 290(12):2319-2323
[2]赵泽茂.数字签名理论[M].北京:科学出版社,2007:1-80
[3]Mohan Atreya.数字签名[M].北京:清华大学出版社,2003:1-50
[4]吴晓云.浅谈数字图像的主动取证技术[J].信息技术,2008, (18):11-12
[5]孙圣和,陆哲明,牛夏牧.数字水印技术及应用[M].北京:科学出版社,2004:10-150
[6]吴金海,林福宗.基于数字水印的图像认证技术[J].计算机学报,2004,27(9):1153-1160
[7]Hany Farid.Creating and Detecting Doctored and Virtual Images Implications to The Child Pornography Prevention Act [OL].TR2004-518, Dartmouth College, Computer Science
[8]Hany Farid.Image Forgery Detection[M].IEEE Signal Processing Magazine, 2009,(3):16-25
[9]Hany Farid.Exposing Digital Forgeries by Detecting Duplicated Image Regions[OL].Tr2004-515, DARTMUTH College, Computer Science
[10]Micah K . Johnson,Hany Farid . Exposing Digital Forgeries Through Chromatic Aberration[C].Proceedings of the Multimedia and Security Workshop 2006,v2006:48-55
[11]Eric Kee,Hany Farid.Printer Profiling for Forensics and Ballistics[C].Proceedings of the 10th ACM Workshop on Multimedia and Security,2008:3-9
[12]Micah K.Johnson,Hany Farid.Detecting Photographic Composites of People[C].Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008,5041:19-33
[13]Hany Farid.Blind Inverse Gamma Correction[J].IEEE Transactions ON Image Processing, 2001,10(10):1428-1433
[14]Johnson Micah K,HanyFarid.Exposing digital forgeries by detecting inconsistencies in lighting Source[C] .Proceedings of the 7th Multimedia and Security Workshop, 2005(2006):1-9
[15]Alin C. Popescu,Hany Farid.Exposing Digital Forgeries by Detecting Traces of sampling[J]. IEEE Transactions ON Signal Processing, 2005,53(2):758-767
[16] Alin C. Popescu, Hany Farid.Exposing Digital Forgeries in Color Filter Array Interpolated Images[J].IEEE Transactions ON Signal Processing, 2005,53(10):3948-3959
[17]Johnson Micah K, Farid, Hany.Exposing digital forgeries through specular highlights on the eye[C] .Information Hiding - 9th International Workshop, IH 2007, Revised Selected Papers,LectureNotes in Computer Science, 2007, 4567: 311-325
[18] Siwei Lyu.Natural Image Statistics for Digital Image Forensics[OL] .http://pqdt.calis.edu.cn/
[19] Tian-Tsong Ng, Shih-Fit Chang.A Model for image splicing[C] .Proceedings - International Conference on Image Processing, ICIP, 2004, 4:1169-1172
[20]Tian-Tsong Ng, Shih-Fu Chang, Sun Qibin.Blind detection of photomontage using higher order statistics[C].IEEE International Symposium on Circuits and Systems, 2004, 5:V688-V691
[21]Hsu Yu-Feng, Chang, Shih-Fu.Detecting image splicing using geometry invariants and camera characteristics consistency[C].IEEE International Conference on Multimedia and Expo,2006, 2006:549-552
[22] Tian-Tsong Ng, Shih-Fu Chang,Mao-Pei Tsui.Lessons Learned from Online Classification of Photo-Realistic Computer Graphics and Photographs[C] .Signal Processing Applications for Public Security and Forensics, 2007
[23]Tian-Tsong Ng, Shih-Fu Chang.Camera Response Function Estimation from a Single-channel Image Using Differential Invariants[OL].http://www.ee.columbia.edu/ln/dvmm/publicationPage//Author//Shih-Fu.Chang.html
[24]Shwin Swaminathan, Min Wu, K. J. Ray Liu.A Pattern Classification Framework for Theoretical Analysis of component forensics[C] .IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2008:1665-1668
[25]Swaminathan, Ashwin,Wu, Min,Liu, K.J. Ray.Component forensics of digital cameras: A non-intrusive approach[C].IEEE Conference on Information Sciences and Systems, 2006:1194-1199
[26]Christine McKay, Ashwin Swaminathan, Hongmei Gou.Image Acquisition Forensics:Forensic Analysis To Identify Imaging Source[C] .IEEE International Conference on Acoustics, Speech and Signal Processing– Proceedings, 2008:1657-1660
[27] Hongmei Gou, Ashwin Swaminathan, Min Wu.Noise Features For Image Tampering Detection And Steganalysis[C] .Proceedings - International Conference on Image Processing, 2006, 6: VI97-VI100
[28] Ashwin Swaminathan, Min Wu, K. J. Ray Liu.Digital Image Forensics via Intrinsic Fingerprints[J].IEEE Transactions on Information Forensics and Security, 2008,3(1):101-117
[29]Ashwin Swaminathan, Min Wu, K. J. Ray Liu.Intrinsic Fingerprints for Image Authentication and Steganalysis[C] .Proceedings of SPIE - The International Society for Optical Engineering,2007: v 6505
[30]Ashwin Swaminathan, Min Wu, K. J. Ray Liu.A Component Estimation Framework for InformationForensics Multimedia Signal Processing[C].IEEE 9th Workshop on Digital Object Identifier, 2007:397– 400
[31] Hongmei Gou, Ashwin Swaminathan, Min Wu.Noise Features for Image Tampering Detection and Steganalysis[C].IEEE International Conference on Image Processing, 2007, 6:VI 97-VI100
[32]Swaminathan, A, Min Wu, Liu, K.J.R.Non-Intrusive Forensic Analysis of Visual Sensors Using Output Images[C] .IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2006:V401-V404
[33] Ashwin Swaminathan, Min Wu and K. J. Ray Liu.Optimization of input pattern for semi non-intrusive component forensics of digital cameras[C] .IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2007, 2:II225-II228
[34]Hongmei Gou, Ashwin Swaminathan, Min Wu.Robust scanner identification based on noise features.[C].Proceedings of SPIE - The International Society for Optical Engineering, 2007:6505,
[35]Jan Luká?, Jessica Fridrich,Miroslav Goljan.Detecting Digital Image Forgeries Using Sensor Pattern Noise[C].Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 6072, 60720
[36]Jessica Fridrich, David Soukal,Jan Lukás.Detection of Copy-Move Forgery in Digital Images[OL] .http://www.ws.binghamton.edu/fridrich/publications.html
[37]Jan Luká?, Jessica Fridrich.Estimation of primary quantization matrix for steganalysis of double-compressed JPEG images[C] .Proceedings of SPIE - The International Society for Optical Engineering, 2008: v6819
[38]Ismail Avcibas, Sevinc Bayram, Nasir Memon.A classifier Design For Detecting Image Manipulations[C].Proceedings - International Conference on Image Processing,2004,4:2645-2648
[39]Bayram Sevinc,Sencar Husrev Taha, Memon Nasir.An efficient and robust method for detecting copy-move forgery[C] .IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2009:1053-1056
[40]Y. Sutcu, B. Coskun. H.T. Sencar,N Memon.Tamper detection based on regularity of wavelet transform coefficients[C] . International Conference on Image Processing, 2007,1:I397-I400
[41]Sevin? Bayram,Smail Avc?bas,Bülent Sankur.Image manipulation detection[J] .Journal of Electronic Imaging ,2006,15(4):041102-01—041102-17
[42]Yun Q. Shi,Chunhua Chen,Wen Chen.A Natural Image Model Approach to Splicing Detection[C] .Proceedings of the Multimedia and Security Workshop, 2007: 51-62
[43]孙堡垒,周琳娜,张茹.基于Benford的高斯模糊篡改取证[J].计算机研究与发展,2009, 6(Suppl.):211—216
[44]周琳娜,王东明,郭云彪,杨义先.基于数字图像边缘特性的形态学滤波取证技术[J].电子学报, 2008,36(6):1047-1051
[45]吴琼,李国辉,孙韶杰,涂丹.基于小波和奇异值分解的图像复制伪造区域检测[J].小型微型计算机系统,2008,29(4):730-733
[46]吴琼.数字图像盲取证对样本合成修复应用的篡改区域检测算法[J].自动化学报,2009,35(5):239-243
[47]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(12A):2451-2454
[48]朱秀明,宣国荣,姚秋明.信息取证中图像重采样检测[J].计算机应用,2006,26(11):2596-2601
[49]骆伟祺,黄继武,丘国平.鲁棒的区域复制图像篡改检测技术[J].计算机学报, 2007,30(11):1998-2007
[50]王俊文,刘光杰,张湛.图像区域复制篡改快速鲁棒取证[J].自动化学报,2009,35(12):1488-1495
[51]王玉平,李生红,赵峰.基于AdaBoost的计算机生成图像检测算法[J] .计算机仿真,2008,25(7):220-222
[52]陈英,赵鹏,王瑀屏.基于边界检测的虚假图像盲识别算法[J].软件学报,2008, 19(12sup):59?68
[53]Tian-Tsong Ng,Shih-Fu Chang, Ching-Yung Lin.Passive-blind Image Forensics[J].Multimedia Security technologies for digital rights, Elsevier,2006:1-30
[54]吴琼.面向真实性检测的数字图像盲取证方法研究[D].长沙:国防科技大学,2008
[55]PIM and PRCG图像数据库.PIM and PRCG图像数据库[OL].http://www.ee.columbia.edu/trustfoto
[56]蒋刚毅,黄大江,王旭.图像质量评价方法研究进展[J].电子与信息学报,2010,32(1):219-225
[57]Dun-Yu Hsiao,Soo-Chang Pei.Detecting digital tampering by blur estimation[C] .Sadfe’05, Taipei, Taiwan, 2005:264-278
[58]Y.Sutcu, B.Cosk.Tamper detection based on regularity of wavelet transform coefficients[C].ICIP2007, San Antonio, Texas, 2007:397-400
[59]AlinC.Popescu,HanyFarid.Statisticaltoolsfordigitalforensics[OL].http://www.cs.dartmouth.edu/farid/research/
[60]He J F,Lin Z C,Wang L F . Detecting doctored JPEG images via DCT coefficient analysis[C].Proceedings of 9th European Conference on Computer Vision, Graz, Austria, 2006:423-435
[61]YE S, SUN Q, CHANG E C.Detecting digital image forgeries by measuring inconsistencies of blocking artifacts[C] .In: Proceedings of 2007 IEEE International Conference on Multimedia and Expo Washington, DC,IEEE Computer Society, 2007:12-15
[62]Micah K. J Johnson, Hany Farid.Exposing digital forgeries in complex lighting environments[J].IEEE Transactions on Information Forensics and Security, 2007,2(3):450-461
[63]Oya Celiktutan, Ismail Avcibas,Bulent Sankur.Blind identification of Cellular Phone Cameras[C].SPIE 2007, San Jose, CA, USA, 2007:1-4
[64]王渝霞.试论静态图像的原始性检验[J].四川警官高等专科学校学报,2006,18(1):68-71
[65]老编.质疑必须以认真的科学态度来分析解读[OL] .http://www.xitek.com/
[66]L.A.Zadeh.Fuzzy Sets[J].Information and Control,1965,8(3):338-353
[67]郭桂蓉.模糊模式识别[M].长沙:国防科技大学出版社,1992:134-159.
[68]Pal.S.k,King.R.A.Image Enhancement using Smoothing width Fuzzy Set[J].IEEE Trans,Syst,Man,Cybern,1981,SMC-11,7:494-501.
[69]Z Pawlak.Rough Sets[J].International Journal of Computer and Information Sciences.2000,1882(11):341-356
[70]徐立中.数字图像的智能信息处理[M].2版.长沙:国防工业出版社,2007:42-63
[71]褚健,荣冈.流程工业综合自动化技术[M].北京:机械工业出版社,2004:157-159
[72]庄天戈.CT原理与算法[M].上海:上海交通大学出版社,1992:6—50
[73]鲁珂,赵继东,叶娅兰.保局投影算法在图像检索中的应用研究[J].计算机应用研究,2006(12):56-58
[74]孙强,张维娜,姚弘奕.基于Radon方向投影特征的文物真伪鉴别方法[J].吉林工业大学自然科学学报,2001,31(2):82-85
[75]马惠敏.基于投影的灰度图像特征提取[M].光学技术,2002,28(6):561-562
[76]桂志国,薄瑞峰,韩炎.基于投影特征的图像匹配的快速算法[J]华北工学院测试技术学报. Vol.14, No.1,2000:18-20
[77]周治紧,李玉鑑.基于投影归一化的字符特征提取方法[J].计算机工程,2006,32(2):197-199
[78]赵振勇,王保华,王力.人脸图像的特征提取[J].计算机技术与发展[J],2007,17(5):221-224
[79]郭瑞,张淑玲,汪小芬.人脸识别特征提取方法和相似度匹配方法研究[J].计算机工程,2006,32(11):225-227
[80]祝磊,朱善安.人脸识别的一种新的特征提取方法[J].光电工程,2007,34(6):122-125
[81]全书海,薛志华,王琴.基于投影图像分布特征的车牌定位算法研究与实现[J].武汉理工大学学报,2005,29(6):879-882
[82]赵春明,石跃祥.利用投影特征高速识别车牌中的汉字[J].计算机工程与应用,2005(19):207-209
[83]刘宁钟,杨静宇.基于投影算法的二维条码识别[J].计算机工程,2002,28(9):32-33
[84]王敬东,徐亦斌,沈春林.一种新的任意角度旋转的景象匹配方法[J].南京航空航天大学学报,2005,37(1):6-10
[85]Lin Y H,Chen C H,Wei C C.New method for subpixel image matching with rotation invariance by combining the parametric template method and the ring projection transform process[J].Optical Engineering, 2006,45(6)
[86]徐亦斌,王敬东,李鹏.基于圆投影向量的景象匹配方法研究[J].系统工程与电子技术,.2005,27(10):1725-1728
[87]王姮,刘冉,解兴哲.新型图像角度匹配方法及其应用研究[J].计算机工程与应用,2007,47(4):208-211
[88]M. S. CHOI.A novel two stage template matching method for rotation and illumination invariance [J].Pattern Recongnition,2002,35:119-129
[89]Lisa Gottesfeld Brown.A Survey of Image Registration Techniques[J].ACM Computing Sureveys, 1992,24(4)
[90]J.P.Lewis.Fast Normalized Cross-Correlation[OL].http://www.docin.com/p-65883159.html
[91]D.I. Barnea, H.F.Silverman.A class of algorithms for fast digital image registration[J].IEEE Transactions on Computing,1972,21:179-186
[92]杨占龙.基于特征点的图像配准与拼接技术研究[D].西安:西安电子科技,2008
[93]Harris C,Stephens.A combined coner and edge detector[C] .Roc.4the Alvey Vision Conf, 1988:147-151
[94]PRESS W H.The art of scientific computiong[M].Cambridge: Cambridge University Press, 1986:498-546
[95]Mikolajczyk, K, Schmid, C.Indexing based on scale invariant interest points[C] .Proceedings of the IEEE International Conference on Computer Vision, 2001,1:525-531
[96]Mikolajczyk, K. Tuytelaars T., Schmid C. A comparison of affine region detectors[J]. International Journal of Computer Vision, v 65, n 1-2, p 43-72, November 2005
[97]杨健.基于Harris-Affine特征的图像检索系统研究与实现[D].大连:大连理工大学,2008
[98]D.G. Lowe.Distinctive image features from scale invariant keypoints[J].International Journal of Computer Vision, 2004,60(2):91-110
[99]Lindeberg T. Scale-space for discrete signal[J].IEEE Transaction PAMI,1980,207:187-207
[100]Guoshen Yu.Jean-Michil Morel.A fully affine invariant image comparison method[C] .The 34th International Conference on Acoustics, Speech, and Signal Processing, 2009:1597-1600
[101]Jean-Michil Morel,Guoshen Yu.ASIFT:A New Framework For Fully Affine Invariant Image Comparison[OL].http://www.cmap.polytechnique.fr/~yu/research/ASIFT/demo.html
[102]Sebastian HS,Lee DD.The manifold ways of perception[J].Science,2000,290(12):2268-2269
[103]Roweis ST, Saul L K.Nonlinear dimensionality analysis by locally linear embedding[J].Science, 2000, 290(12):2319-2323
[104]Tenenbaum JB,de Silva V, Langford JC.A global geometric framework for nonlinear dimensionality reduction[J].Scinece, 2000, 290(12):2319-2323