摘要
以数字图像、数字视频为代表的数字多媒体资源具有易编辑、易复制、易传播等特性，普通用户借助通用多媒体编辑软件便可对其进行非常逼真的编辑或篡改，且不会留下直观的视觉痕迹。数字多媒体被动取证技术就是在这种背景下应运而生的，它是在无预先嵌入特定指示性信息的情况下，“被动”地检测数字媒体的来源，及其真实性和完整性的新型认证技术。本文以数字视频资源为研究对象，围绕数字视频来源取证与典型篡改检测两个核心问题，对数字视频被动取证技术展开了较为深入的研究。主要创新性工作包括以下五个方面：
(1)提出了一种基于编码开放模块差异的视频来源检测算法。通过深入分析现有视频压缩编码标准体系的特点，以视频编码标准中的两个开放模块——码率控制与运动预测为研究重点，分析并总结在MPEG-2编码标准下，不同来源的编码器之间的差异，然后基于这些差异构建了三类针对性的特征集，最后引入支持向量机实现多类视频资源的来源鉴别和追溯。
(2)提出了一种针对MPEG-2标准的视频双压缩检测算法。通过定性和定量分析，发现MPEG-2标准下的数字视频在经历二次压缩编码过程后，其DCT系数分布发生规律性的变化，并以此为依据构建检测特征集，结合高性能分类算法，最终实现在多种编码参数条件下的二次压缩编码过程检测。
(3)提出了一种针对MPEG-2格式转为MPEG-4格式的视频转码检测算法。在给出了重建DCT系数分布模型的基础之上，定量分析了视频转码操作对DCT系数分布造成的直接影响，并定义了全局和局部检测特征，其在不同的编码参数设置条件下，均能很好地区分原始视频资源与转码视频资源。
(4)提出了一种基于高频能量变化的视频帧编辑检测算法。视频帧编辑操作会破坏原始视频帧类型分布，导致篡改视频帧序列中存在周期性的帧类型转变。同时不同编码类型帧之间对应的高频能量也因非线性量化而存在差异。本文通过构建帧高频能量特征，并引入Morlet小波分析其变化规律，在判断待测视频是否经过了帧编辑操作的同时，能够进一步探测帧编辑位置。
(5)提出了一种基于频域残差的视频帧滤波检测算法。平滑滤波操作在消除噪声的同时，也会导致视频帧的高频信息大量损失，检测算法通过引入再次滤波过程，利用频域残差来分析视频帧的高频损失程度。最后，借助Radon变换与曲线建模的方法，能有效地区分原始视频帧与经过平滑处理的视频帧。
Since digital multimedia resources, such as digital pictures and digital videos, areeasily manipulated, duplicated, and conveniently transmitted, the amateur caneffectively edit or tamper with digital multimedia resources by means of commonmultimedia processing and editing software without visual clues of forgery. Blinddigital media forensics technology becomes a new topic in the field of informationsecurity, which passively identifies the origination, authenticity and integrity of thedigital media without the aid of previous embedded information. This dissertationwhich belongs to the field of passive blind video forensics, focuses on two issues:source digital video identification and detection of novel tampering operation. Themain achievements reveal as following:
(1) A new source video coding system identification is proposed based on thefeatures in the video stream. More specifically, it takes full advantage of thedifferent characteristics in the rate control module and the motion predictionmodule, which are two main open parts in the MPEG-2video compressionstandard. Three feature sets are extracted, and combined with a support vectormachine classifier to build an intelligent computing system for video sourceidentification.
(2) A method is presented to detect double MPEG-2compression. Throughqualitatively and quantitatively analyzing the variation of DCT coefficientsduring double MPEG-2compression in depth, it is found that the distribution ofquantized DCT coefficients regularly changes. Then, a new detection algorithm isheuristically designed based on the differences in statistic distributions ofquantized DCT coefficients between the single compression and the doublecompression. Experiment results show that the proposed scheme can effectivelydetect doubly MPEG-2compressed videos under the conditions of diverse codingparameters.
(3) A detection algorithm for video transcoding is proposed based on a model forreconstructed Discrete Cosine Transform (DCT) coefficients which is formulatedwith the distribution of quantization parameters. This model is utilized to revealthat the distribution of quantized DCT coefficients in the transcoded video has aseries of local maximas or minimas with a period. Finally, two sets of features are proposed to detect the periodical variation. Experimental results demonstrate thevalidity and effectiveness of the proposed approach.
(4) A new method is presented to detect frame tampering based on thehigh-frequency features of reconstructed DCT coefficients in the tamperedsequences. The frame tampering operation impacts the distribution of the codingtype of frame in the original video, and then causes new periodicities in thedistribution of the coding type of frame in the tampered video. Since differentkinds of frames have different high-frequency features of DCT coefficients due tothe non-linear quantization, the distribution of high-frequency features will alsopresent periodicities. By Morlet wavelet tools, a coarse-to-fine location approachis proposed to precisely locate breakpoints in the tampered sequences.
(5) A novel algorithm for detecting smoothing filtering in digital videos is proposedbased on the frequency residual. The suspected frame is re-filtered with aGaussian low-pass filter, and the difference between the initial frame and there-filtered frame in Fourier domain is called the frequency residual. Thesmoothing filtering not only eliminates the noise, but also cuts down the highfrequency information, and different filters cause different levels of distortion,which respond to different properties of the frequency residual. Finally, theRadon transform and curve modeling method are utilized to analyze thefrequency residual for distinguishing the original frame and the smoothed frame.The experimental results show that the proposed algorithm can not only detectthree typical smoothing spatial filters, including Gaussian filter, average filter,and median filter, but also can predict parameters of these filters to complementthe existing state-of-the-art methods.
引文
[1] International Organization for Standardization, ISO7498-2: Informationprocessing system-Open Systems Interconnection-Basic Reference Mode-Part2: Security architecture,1989
[2] Mohan Atreya，数字签名，北京：清华大学出版社，2003：1-50
[3]孙圣和，陆哲明，牛夏牧，数字水印技术及应用，北京：科学出版社，2004：10~150
[4]张春田，苏育挺，管晓康.多媒体数字水印技术，通信学报，2000，21（9）：546~552
[5]吴金海，林福宗，基于数字水印的图像认证技术，计算机学报，2004，27（9）：1153~1161
[6]陈明奇,钮心忻，数字水印的研究进展和应用，通信学报，2001，22（5）：71~79
[7]尹浩，林闯，邱锋，数字水印技术综述，计算机研究与发展，2005，42（7）：1093~1099
[8]宋玉杰，谭铁牛，基于脆弱性数字水印的图像完整性验证研究，中国图象图形学报，8（1）：1~7
[9]张静，张春田，用于图象认证的数字水印技术，中国图象图形学报，2003，8（4）：367~378
[10]I.J. Cox，M.L. Miller，J.a. Bloom，数字水印，北京：电子工业出版社，2003
[11]K. S. Choi, E. Y. Lam, K. K. Y. Wong, Source Camera Identification UsingFootprints From Lens Aberration. Proc. the International Society for OpticalEngineering, San Jose, USA,2006,(60690):60690J-1~8
[12]K. S. Choi, E. Y. Lam, K. K. Y. Wong, Automatic Source Camera IdentificationUsing the Intrinsic Lens Radial Distortion, Optics Express,2006,14(24):11551~11565
[13]Van Lt, Emmanuel S, Kankanhalli M, Identifying Source Cell Phone UsingChromatic Aberration. Proc. IEEE International Workshop on Multimedia&Expo,Beijing, China,2007,883~886
[14]Bayram, S., Sencar, H., Memon, N., et al., Source Camera Identification Basedon CFA Interpolation. Proc. IEEE International Conference on Image Processing,Genova, Italy,2005,(3):69~72
[15]Y. Long, Y. Huang, Image Based Source Camera Identification UsingDemosaicking. Proc. IEEE Workshop on Multimedia Signal Processing, Victoria,Canada,2006,419~424
[16]A. Swaminathan, M. Wu, K. J. Ray Liu, Nonintrusive Component Corensics ofVisual Sensors Using Output Images, IEEE Transactions on InformationForensics and Security,2007,2(1):91~106
[17]A. Swaminathan, M. Wu, K. J. Ray Liu, Optimization of Input Pattern for SemiNon-Intrusive Component Forensics of Digital Cameras. Proc. IEEE InternationalConference on Acoustics, Speech, and Signal Processing, Honolulu, USA,2007,(2):225~228
[18]Holst G-C, Book review: CCD arrays, cameras, and displays, Optics andPhotonics News,1997,8(4):1~54
[19]A.E.Dirik,, H.T.Sencar, N.Memon, Source Camera Identification Based onSensor Dust Characteristics. Proc. IEEE Workshop on Signal ProcessingApplications for Public Security and Forensics, Washingto, USA,2007,1~6
[20]J. Lukas, J. Fridrich, M. Goljan, Digital Camera Identification from SensorPattern Noise, IEEE Transactions on Information Forensics and Security,2006,1(2):205~214
[21]J. Fridrich, M. Chen, M. Goljan, Digital Imaging Sensor Identification (FurtherStudy). Proc. the International Society for Optical Engineering, San Jose, USA,2007,(6505):65050P-1~13
[22]J. Fridrich, M. Goljan, M. Chen, Identifying Common Source Digital Camerafrom Image Pairs. Proc. IEEE International Conference on Image Processing, SanAntonio, USA,2007,(VI):125~128
[23]C. T. Li, Source Camera Identification Using Enhanced Sensor Pattern Noise.Proc. the200916th IEEE International Conference on Image Processing, Cairo,Egypt,1509~1512
[24]K. L. Mehdi, H. T. Sencar, N. Memon, Blind Source Camera Identification. Proc.International Conference on Image Processing, Singapore,2004,(1):709~712
[25]M. J. Tsai, G. H. Wu, Using Image Features to Identify Camera Sources. Proc.IEEE International Conference on Acoustics, Speech, and Signal Processing,Toulouse, France,2006,(II):297~300
[26]H. Farid, Digital Image Ballistics From JPEG Quantization, Technical Report,Dartmouth College, Computer Science,2008, Tr2006-583
[27]K. S. Choi, E. Y. Lam, K. K. Y. Wong, Source Camera Identification by JPEGCompression Statistics for Image Forensics. Proc.2006IEEE Region10Conference, Hong Kong, China,2006,1~4
[28]E. Kee, H. Farid, Digital Image Authentication from Thumbnails. Proc. theInternational Society for Optical Engineering, San Jose, USA,2010,(7541):75410E-1~10
[29]J. Fridrich, D. Soukal, J. Lukas, Detection of Copy-Move Forgery in DigitalImages. Proc. of Digital Forensic Research Workshop, Cleveland, OH, USA,2003,134~137
[30]Popescu A, Farid H, Exposing Digital Forgeries by Detecting Duplicated ImageRegions, Technical Report, Department of Computer Science, Dartmouth College,2004, Tr2004-515
[31]G. Li, Q. Wu, D. Tu, S. Sun, A Sorted Neighborhood Approach for DetectingDuplicated Regions in Image Forgeries Based on DWT and SVD. Proc. IEEEICME, Beijing China,2007,1750~1753
[32]A. Langille, M. Gong, An Efficient Match-Based Duplication DetectionAlgorithm. Proc. the3rd Canadian Conference on Computer and Robot Vision,Quebec City, Canada,2006,1~64
[33]B. Dybala, B. Jennings, D. Letscher, Detecting Filtered Cloning in Digital Images.Proc. the9th Workshop on Multimedia&Security, Dallas, Texas, USA,2007,43~50
[34]B. Mahdian, S. Saic, Blind Methods for Detecting Image Fakery, IEEEAerospace and Electronic Systems Magazine,2010,25(4):18~24
[35]H. Huang, W. Guo, Y. Zhang, Detection of Copy-Move Forgery in DigitalImages Using Sift Algorithm. Proc. IEEE Pacific-Asia Workshop onComputational Intelligence and Industrial Application, Wuhan, China,2008,272~276
[36]S. Bayram, T. Sencar, N. Memon, An Efficient and Robust Method for DetectingCopy-Move Forgery. Proc. IEEE ICASSP, Taipei, Taiwan,2009.1053~1056
[37]I. Amerini, L. Ballan, R. Caldelli, et al., Geometric Tampering Estimation byMeans of A SIFT Based Forensic Analysis. Proc. IEEE ICASSP, Dallas, TX,USA,2010,1702~1705
[38]X. Pan, S. Lyu, Detecting Image Duplication Using SIFT Features. Proc. IEEEICASSP, Dallas, TX, USA,2010,1706~1709
[39]A. Sarkar, L. Nataraj, B. S. Manjunath, Detection of Seam Carving andLocalization of Seam Insertions in Digital Images. Proc. the11th Acm Workshopon Multimedia and Security, Princeton, NJ, USA,2009,107~116
[40]C. Fillion, G. Sharma, Detecting Content Adaptive Scaling of Images forForensic Applications. Proc. the International Society for Optical Engineering,San Jose, CA, USA,2010,(7541):75410Z-1~12
[41]Q. Wu, S. J. Sun, W. Zhu, et al., Detection of Digital Doctoring inExemplar-Based Inpainted Images. Proc. the7th International Conference onMachine Learning and Cybernetics, Kunming, China,2008,(3):1222~1226
[42]I. Avcibas, S. Bayram, N. Memon, et al., A Classifier Design for Detecting ImageManipulation. Proc. International Conference on Image Processing, Singapore,2004,(4):2645~2648
[43]H. Farid, Exposing Digital Forgeries in Scientific Images. Proc. ACM Workshopon Multimedia and Security, Geneva, Switzerland,2006,29~36
[44]M. Stamm, J. R. Liu, Forensic Detection of Image Manipulation Using StatisticalIntrinsic Fingerprints, IEEE Transaction on Information Forensics and Security,2010,5(3):492~506
[45]T. T. Ng, S. F. Chang, Q. Sun. Blind Detection of Photomontage Using HigherOrder Statistics. Proc. IEEE International Symposium on Circuits and Systems,Vancouver, BC, Canada,2004,688~691
[46]W. Chen, Y. Q. Shi, W. Su, Image Splicing Detection Using2-D PhaseCongruency and Statistical Moments of Characteristic Function. Proc. theInternational Society for Optical Engineering, San Jose, CA, USA,2007,(6505):65050R-1~8
[47]C. Chen, Y. Q. Shi, G. Xuan, Steganalyzing Texture Images. IEEE InternationalConference on Image Processing, San Antonio, TX, USA,2007, II-153~156
[48]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
[49]W. Chen, Y. Q. Shi, G. Xuan, et al., Steganalysis versus Splicing Detection. Proc.the6th International Workshop on Digital Watermarking, Guangzhou, China,2007,158~172
[50]M. K. Johnson, H. Farid, Exposing Digital Forgeries by Detecting Inconsistenciesin Lighting. Proc. ACM Multimedia and Security Workshop, New York, USA,2005,(2006):1~9
[51]M. K. Johnson, H. Farid, Exposing Digital Forgeries in Complex LightingEnvironments, IEEE Transactions on Information Forensics and Security,2007,2(3):450~461
[52]M. K. Johnson, H. Farid, Exposing Digital Forgeries through Specular Highlightson the Eye. Proc. the9th International Workshop on Information Hiding, SaintMalo, France,2007,(4567):311~325
[53]M. K. Johnson, H. Farid, Detecting Photographic Composites of People. Proc. the6th International Workshop on Digital Watermarking, Guangzhou, China,2007.19~33
[54]J. Lukas, J. Fridrich, M. Goljan, Detecting Digital Image Forgeries Using SensorPattern Noise. Proc. the International Society for Optical Engineering, San Jose,USA,2006,(6072):60720Y-1~11
[55]A. C. Popescu, H. Farid, Exposing Digital Forgeries by Detecting Traces ofRe-Sampling. IEEE Transactions on Signal Processing,2005,53(2):758~767
[56]M. Kirchner, Fast and Reliable Resampling Detection by Spectral Analysis ofFixed Linear Predictor Residue. Proc. the Multimedia and Security Workshop,New York, USA,2008,11~20
[57]L. Nataraj, A. Sarkar, B. S Manjunath, Improving Re-Sampling Detection byAdding Noise, Proc. the International Society for Optical Engineering, San Jose,CA, USA,2010,(7541):75410I-1~11
[58]M. Kirchner, T. Gloe, On Resampling Detection in Re-Compressed Images. Proc.the First IEEE Workshop on Information Forensics and Security, London, UK,2009,21~25
[59]H. Farid, Exposing Digital Forgeries from JPEG Ghosts. IEEE Transactions onInformation Forensics and Security,2009,4(1):154~160
[60]A. C. Popescu, H. Farid, Statistical Tools for Digital Forensics. Proc. the6thInternational Workshop on Information Hiding, Toronto, Canada,2004,128~147
[61]B. Li, Y. Q. Shi, J. Huang, Detecting Double Compressed JPEG Images by UsingMode Based First Digit Features. Proc. IEEE Internation Workshop onMultimedia Signal Processing, Cairns, Queensland, Australia,2008,730~735
[62]J. He, Z. Lin, L. Wang, et al., Detecting Doctored JPEG Images via DCTCoefficient Analysis. Proc. ECCV2006, Graz, Austria,2006,423~435.
[63]J. Lukas, J. Fridrich, Estimation of Primary Quantization Matrix in DoubleCompressed JPEG Images. Proc. Digital Forensic Research Workshop2003,Cleveland, OH, USA,2003,(2):67~84
[64]F. Huang, J. Huang, Y. Q. Shi. Detecting Double JPEG Compression with theSame Quantization Matrix, IEEE Transactions on Information Forensics andSecurity,2010,5(4):848-56
[65]W. Luo, Z. Qu, J. Huang, et al., A Novel Method for Detecting Cropped andRecompressed Image Block. Proc. ICASSP2007, Honolulu, HI, USA,2007,(II):217~220
[66]M. Barni, A. Costanzo, L. Sabatini, Identification of Cut&Paste Tampering byMeans of Double-JPEG Detection and Image Segmentation. Proc. IEEEInternational Symposium on Circuits and Systems, Paris, France,2010,1687~1690
[67]Y. L. Chen, C. T. Hsu, Image Tampering Detection by Blocking PeriodicityAnalysis in JPEG Compressed Images. Proc. IEEE10th Workshop on MultimediaSignal Processing, Cairns, Qld, Australia,2008,803~808
[68]Y. L. Chen, C. T. Hsu, Detecting Recompression of JPEG Images Via PeriodicityAnalysis of Compression Artifacts for Tampering Detection, IEEE Transactionson Information Forensics and Security,2011,6(2):396~406
[69]T. Bianchi, A. Piva, Detection of Nonaligned Double JPEG Compression Basedon Integer Periodicity Maps, IEEE Transaction on Information Forensics andSecurity,2012,7(2):842~848
[70]T. Bianchi, A. Piva, Image Forgery Localization via Block-Grained Analysis ofJPEG Artifacts, IEEE Transactions on Information Forensics and Security,2012,7(3):1003~1017
[71]M. K. Johnson, H. Farid, Exposing Digital Forgeries through ChromaticAberration. Proc. the Multimedia and Security Workshop, Geneva, Switzerland,2006,48~55
[72]A. C. Popescu, H. Farid, Exposing Digital Forgeries in Color Filter ArrayInterpolated Images, IEEE Transactions on Signal Processing,2005,53(10):3948~3959
[73]M. Chen, M. Goljan, Imaging Sensor Noise as Digital X-Ray for RevealingForgeries. Proc.9th Information Hiding Workshop, Saint Malo, France,2007,342~358
[74]Z. Lin, R. Wang, X. Tang, et al., Detecting Doctored Images Using CameraResponse Normality and Consistency. Proc. IEEE Computer Society Conferenceon Computer Vision and Pattern Recognition, San Diego, CA, USA,2005,1087~1092
[75]Y. F. Hsu, S. F. Chang, Detecting Image Splicing Using Geometry Invariants andCamera Characteristics Consistency. Proc. IEEE International Conference onMultimedia and Expo. Toronto, Canada,2006,549~552
[76]T. T. Ng, S. F. Chang, M. P. Tsui, Using Geometry Invariants for CameraResponse Function Estimation. Proc. IEEE Computer Society Conference onComputer Vision and Pattern Recognition, Minneapolis, MN, USA,2007,236~243
[77]Y. F. Hsu, S. F. Chang, Image Splicing Detection Using Camera ResponseFunction Consistency and Automatic Segmentation. Proc. IEEE InternationalConference on Multimedia and Expo, Beijing, China,2007,28~31
[78]K. Kurosawa, K. Kuroki, N. Saitoh, CCD Fingerprint Method-identification of AVideo Camera from Video Taped Images. Proc. Internation Conference on ImageProcessing, Kobe, Japan,1999,537~540
[79]J. Fridrich, M. Chen, M. Goljan, Source Digital Camcorder Identification UsingSensor Photo-Response Non-Uniformity. Proc. SPIE, San Jose, CA, USA,2007,1G~1H.
[80]W. Vanhouten, Z. Geradts, K. Franke, et al., Verification of Video SourceCamera Competition (CAMCOM2010). Proc. Recognizing Patterns in Signals,Speech, Images and Videos, Istanbul, Turkey,2010,22~28
[81]W. Van Houten, Z. Geradts, Using Sensor Noise to Identify Low ResolutionCompressed Videos From Youtube. Proc. IWCF, Hague, Netherlands,2009,104~115
[82]W. Van Houten, Z. Geradts, Source Video Camera Identification FormultiplyCompressed Videos Originating from Youtube. Digital Investigation,2009,6(1-2):48–60
[83]H. Li, S. Forchhammer, MPEG2Video Parameter and No Reference PSNREstimation. Proc. Picture Coding Symposium, Chicago, IL, USA,2009,1~4
[84]Y. Chen, K. S. Challapali, M. Balakrishnan, Extracting Coding Parameters fromPre-Coded MPEG-2Video. Proc. ICIP98. Chicago, IL, USA,1998,360~364
[85]M. Tagliasacchi, S. Tubaro, Blind Estimation of the QP Parameter in H.264/AVCDecoded Video. Proc.11th Internation Workshop on Image Analysis forMultimedia Interactive Services, Piscataway, NJ, USA,2010,1~4
[86]G. Valenzise, M. Tagliasacchi, S. Tubaro, Estimating QP and Motion Vectors inH.264/AVC Video from Decoded Pixels. Proc. the2010ACM Workshop onMultimedia in Forensics, Security and Intelligence, Firenze, Italy,2010,89~92
[87]A. R. Reibman, D. Poole, Characterizing Packet-loss Impairments in CompressedVideo. Proc. ICIP2007, San Antonio, TX, USA,2007,2328~2331
[88]A. R. Reibman, V. A. Vaishampayan, Y. Sermadevi, Quality Monitoring ofVideo over A Packet Network, IEEE Transaction Multimedia,2004,6(2):327~334
[89]G. Valenzise, S. Magni, M. Tagliasacchi, et al., Estimating Channel-InducedDistortion in H.264/AVC Video without Bitstream Information. Proc. the2010Second International Workshop on Quality of Multimedia Experience, Trondheim,Norway,2010,100~105
[90]N. Mondain, R. Caldelli, A. Piva, et al., Detection of Malevolent Changes inDigital Video for Forensic Applications. Proc. the International Society forOptical Engineering, San Jose, CA, USA,2007,(6505):65050T-1~12
[91]C. C. Hsu, T. Y. Hung, C. W. Video Forgery Detection Using Correlation ofNoise Residue. Proc. IEEE10th Workshop on Multimedia Signal Processing.2008,170~174
[92]M. Kobayashi, T. Okabe, Y. Sato, Detecting Forgery from Static-Scene VideoBased on Inconsistency in Noise Level Functions, IEEE Transaction onInformation Forensics and Security,2010,5(4):883-892
[93]A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum Likelihood fromIncomplete Data via the EM Algorithm, Journal of the Royal Statistical Society,1977,1:1-38
[94]W. Wang, H. Farid, Exposing Digital Forgeries in Video by DetectingDuplication. Proc. the Multimedia and Security Workshop, Dallas, TX,2007,34~47
[95]W. Wang, H. Farid, Exposing Digital Forgeries in Interlaced and De-InterlacedVideo, IEEE Transactions on Information Forensics and Security,2007,2(3):438~449
[96]W. Wang, H. Farid, Detecting Re-projected Video. Proc. Information Hiding,Santa Barbara, CA, USA,2008,(5284):72~86
[97]J. Zhang, Y. Su, M. Zhang, Exposing Digital Video Forgery by Ghost ShadowArtifact. Proc. First ACM Workshop on Multimedia Inforensics, Beijing, China,2009,49~54
[98]V. Conotter, J. O’Brien, H. Farid, Exposing Digital Forgeries in Ballistic Motion,IEEE Transactions on Information Forensics and Security,2011,7(1):283~296
[99]W. Wang, H. Farid, Exposing Digital Forgeries in Video by Detecting DoubleMPEG Compression. Proc. the Multimedia and Security Workshop, Geneva,Switzerland,2006,37~47
[100]W. Wang, H. Farid, Exposing Digital Forgeries in Video by Detecting DoubleQuantization. Proc. the11th ACM Multimedia Security Workshop, Princeton, NJ,United states,2009,39~47
[101]W. Chen, Y. Q. Shi, Detection of Double MPEG Compression Based on FirstDigit Statistics, Proc. Digital Watermarking, Busan, South Korea,2008,16~30
[102]W. Luo, M. Wu, J. Huang, MPEG Recompression Detection Based on BlockArtifacts, Proc. the International Society for Optical Engineering, San Jose, CA,USA,2008,(6819):68190X-1~12
[103]A. K. Mikkilineni, P. J. Chiang, G. N. Ali, et al., Printer Identification Based onGraylevel Co-occurrence Features for Security and Forensic Applications. Proc.the International Society for Optical Engineering, San Jose, CA, USA,2005,(5681):430~440
[104]A. K. Mikkilineni, O. Arslan, P. J. Chiang, et al., Printer Forensics Using SVMTechniques. Proc. International Conference on Digital Printing Technologies,Baltimore, MD, USA,2005,223~226
[105]A. K. Mikkilineni, P. J. Chiang, G. N. Ali, et al., Printer Identification Based onTextural Features. Proc. International Conference on Digital PrintingTechnologies, Salt Lake City, UT, USA,2004,306~311
[106]N. Khanna, A. K. Mikkilineni, G. T.C Chiu, et al., Forensic Classification ofImaging Sensor Types. Proc of the International Society for Optical Engineering,San Jose, CA, USA,2007,65050U-1~9
[107]T. Gloe, E. Franz, A. Winkler, Forensics for Flatbed Scanners. Proc. theInternational Society for Optical Engeneering, San Jose, CA, USA,2007,65050U-1~9
[108]H. Gou, A. Swaminathan, M. Wu, Rodust Scanner Identification Based onNoise Features. Proc. the International Society for Optical Engeneering, San Jose,CA, USA,2007,65050S-1~11
[109]S. Lyu, H. Farid, How Realistic Is Photorealistic, IEEE Transactions on SignalProcessing,2005,53(2):845~850
[110]S. Dennie, T. Sencar, N. Memon, Digital Image Forensics For IdentifyingComputer Generated and Digitai Camera Images. Proc. IEEE InternationalConference on Image Processing, Atlanta, GA, USA,2006,2313~2316
[111]T. Laneva, A. De Vries, H. Rohrig. Detecting Cartoons: A Case Study inAutomatic Video-genre Classification. Proc. IEEE International Conference onMultimedia and Expro, Baltimore, MD, USA,2003,449~452
[112]W. Chen, Y. Q. Shi, G. Xuan, Identifying Computer Graphics Using HSV ColorMdel and Statistical Moments of Characteristic Functions. Proc. IEEEInternational Conference on Multimedia and Expro, Beijing, China,2007,1123~1126
[113]T. T. Ng, S. F. Chang, An Online System for Classifying Computer GraphicsImages from Natural Photographs. Proc. The International Society for OpticalEngineering, San Jose, CA, USA,2006,607211-1~9
[114]B. E. Koenig, Authentication of Forensic Audio Recordings, Journal of theAudio Engineering Society,1990,38(1-2):3~33
[115]R. C. Maher, Audio Forensic Examination, IEEE Signal Processing Magazine,2009,26(2):84~94
[116]C. Grigoras, Digital Audio Recording Analysis: the Electric Network Frequency(ENF) Criterion, Speech, Language and the Law,2005,12(1):63~76
[117]C. Grigoras, Applications of ENF Criterion in Forensic Audio, Video,Computer and Telecommunication Analysis, Forensic Science International,2007,167(2-3):136~145
[118]C. Grigoras, Applications of ENF Analysis in Forensic Authentication ofDigital Audio and Video Recordings, Journal of the Audio EngineeringSociety,2009,57(9):643~661
[119]高阳，黄征，徐彻等，基于高阶频谱分析的音频篡改鉴定，信息安全与通信保密，2008，(2)：94~96
[120]姚秋明，柴佩琪，宣国荣等，基于期望最大化算法的音频取证中的篡改检测，计算机应用，2006，26(11)：2598~2601
[121]R. Yang, Q.Y. Shi, J. Huang, Detecting Double Compression of Audio Signal.Proc. the International Society for Optical Engineering, San Jose, CA, USA,2010,(7541):75410K-1~10
[122]M. Kirchner, R. B hme, Hiding Traces of Resampling In Digital Images, IEEETransactions on Information Forensics and Security,2008,3(4):582~592
[123]M. C. Stamm, S. K. Tjoa, W. S. Lin, et al., Anti-forensics of JPEG compression.Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing,Dallas, TX, USA,2010,1694~1697
[124]M. C. Stamm, K. J. R. Liu, Wavelet-based Image Compression Anti-forensics.Proc. IEEE International Conference on Image Processing, Hong Kong, China,2010,1737~1740
[125]M. C. Stamm, K. J. R. Liu, Anti-forensics of Digital Image Compression, IEEETransactions on Information Forensics and Security,2011,6(3):1050~1065
[126]M. C. Stamm, K. J. R. Liu, Anti-forensics for frame deletion/addition in MPEGvideo. Proc. IEEE International Conference on Acoustics, Speech and SignalProcessing, Prague, Czech Republic,2011,1876~1879
[127]M. C. Stamm, W. S. Lin, K. J. R. Liu, Forensics vs. Anti-forensics: A Decisionand Game Theoretic Framework. Proc. IEEE International Conference onAcoustics, Speech and Signal Processing, Kyoto, Japan,2012,1749~1752
[128]M. Goljan, J. Fridrich, Camera Identification from Cropped and Scaled Images.Proc. the International Society for Optical Engineering, San Jose, CA, USA,2008,68190E-1~13
[129]D. K. Hyun, M. J. Lee, S. J. Ryu, et al., Forgery Detection for SurveillanceVideo, the Era of Interactive Media,2012,25~36
[130]D. K. Hyun, S. J. Ryu, M. J. Lee, et al., Source Camcorder Identification FromCropped and Scaled Videos, Proc. the International Society for OpticalEngineering, Burlingame, CA, USA,2012,(8303):83030E-1~8
[131]ISO/IEC IS13818-2: Information Technology—Generic Coding of MovingPictures and Associated Audio Information—Part2: Video,1995(Mpeg-2Video)
[132]G. N. Anthony, J. N. Hwang, A Novel Hybrid Hvpc Mathematical Model RateControl for Low Bit-Rate Stream Video, Signal Processing: ImageCommunication,2002,17(5):423~440
[133]L. Wang, Rate Control for Mpeg Video Coding, Signal Processing: ImageCommunication,2000,15(6):493~511
[134]S. H. Hong, S. J. Yoo, S. W. Lee, et al., Rate Control of MPEG Video forConsistent Picture Quality, IEEE Transaction on Broadcasting,2003,49(1):1~13
[135]Z. Lin, J. He, X. Tang, et al., Fast, Automatic and Fine-Grained TamperedJPEG Image Detection via DCT Coefficient Analysis, Pattern Recognition,2009,42(11):2492~2501
[136]K. Mayer-Patel, B. Smith, L. Rowe, the Berkeley Software MPEG-1VideoDecoder, ACM Transactions on Multimedia Computing, Communications, andApplications,2005,1(1):110~125
[137]Xvid Codec: A Popular MPEG-4Encoder,[Online]. Available:HTTP://www.xvid.Org/Downloads.15.0.Html.
[138]R. C. Reininger, J. D. Gibson, Distributions of the Two Dimensional DCTCoefficients for Images, IEEE Transactions on Communications,1983,31(6):835~839
[139]T. J. Kozubowski, K. Podgorski, A Class of Asymmetric Distributions,Actuarial Research Clearing House,1999,(1):113~134
[140]C. Torrence, G. P. Compo, A Practical Guide to Wavelet Analysis, BulletinAmerican Meterological Society,1998,79(1):61~78
[141]A. Swaminathan, M. Wu, K. J. Liu, Digital Image Forensics via IntrinsicFingerprints, IEEE Transactions on Information Forensics and Security,2008,3(1):101~117
[142]W. Chuang, A. Swaminathan, M. Wu, Tampering Identification UsingEmpirical Frequency Response. Proc. IEEE International Conference onAcoustics, Speech and Signal Processing, Taipei, Taiwan,2009,1517~1520
[143]A. D. Ker, R. B hme, Revisiting Weighted Stego-Image Steganalysis. Proc. theInternational Society for Optical Engineering, San Jose, CA, USA,2008,681905-1~17
[144]M. Kirchner, R. B hme, Hiding Traces of Resampling in Digital Images, IEEETransactions on Information Forensics and Security,2008,4(3):582~592
[145]M. Kirchner, J. Fridrich, On Detection of Median Filtering in Digital Images.Proc. the International Society for Optical Engineering, San Jose, CA, USA,2010,(7541):754110-1~12
[146]T. Pevny, P. Bas, J. Fridrich, Steganalysis by Subtractive Pixel AdjacencyMatrix, IEEE Transactions on Information Forensics and Security,2010,5(2):215~224
[147]G. Cao, Y. Zhao, R. Ni, et al., Forensic Detection of Median Filtering in DigitalImages. Proc. IEEE International Conference on Multimedia and Expo, SuntecCity, Singapore,2010,89~94
[148]C. Chen, J. Ni, R. Huang, et al., Blind Median Filtering Detection UsingStatistics in Difference Domain. Proc. Information Hiding, Berkeley, CA, USA,2012,1~15
[149]H. Yuan, Blind forensics of median filtering in digital images, IEEETransactions on Information Forensics and Security,2011,6(4):1335~1345
[150]X. Kang, M. C. Stamm, A. Peng, et al., Robust Median Filtering ForensicsBased on the Autoregressive Model of Median Filter Residual. Proc. APSIPA,Hollywood, CA, USA,2012,9~19
[151]S. R. Deans, Applications of the Radon Transform, Wiley IntersciencePublications,1983
[152]P. Bas, T. Furon. Break Our Watermarking System July2007[Online].http://bows2.gipsa-lab.inpg.fr,2nd ed