摘要
当今信息时代,随着计算机、网络技术、多媒体技术以及廉价数码设备的广泛使用,人们可以更加方便、快捷的获取一幅数字图像,在人们享受数字图像所带来快乐的同时,功能强大的图像处理及编辑软件应运而起,它们的出现和不断完善使得一些怀有不良目的的人可以轻而易举地对一幅数字图像作品进行恶意篡改,以此来掩饰图像的真实面貌,从而达到某种特定目的。尤其是当被篡改后的图像应用到新闻媒体、司法以及军事等领域时,势必会影响新闻和娱乐信息的真实性、司法审判的公正性、军事情报的可靠性,无论于国于民都会产生不可估量的损失。因此研究鉴别数字图像内容真实性的新技术,无论对重树人们对新闻媒体的信心,还是对维护社会公平和正义都具有深远的意义。
本文在国家自然基金项目和天津市重点自然基金项目的支持下,重点围绕鉴别数字图像真伪这一主线,从图像内容篡改盲检测以及反取证技术两方面分别进行了研究,其主要研究成果如下:
1.针对当前区域复制一粘贴篡改检测方法所提取的特征维数过大缺点,提出了一种基于DCT系数能量分布的篡改检测算法。将图像从空域转化到频域,对图像进行分块,利用离散余弦变换及其变换后的离散余弦系数的低频、中频、高频能量分布情况,依次对每个子块提取4维匹配特征,保证了算法具有较低的复杂度。并通过实验对所提方法在检测精度、高斯白噪声、高斯模糊以及多处篡改操作上进行性能分析,说明了所提算法具有较高的鲁棒性。
2.提出了一种能抵抗旋转操作的图像盲取证方法,通过对图像统计特征的分析,提出了利用伪Zernike矩作为图像特征,以此来抵抗图像篡改中的旋转操作。将原始图像进行划分,利用低阶伪Zernike矩代表图像轮廓,高阶伪Zernike矩代表图像细节的特点,对每个子块构造低阶伪Zernike矩,提高了算法对噪声的鲁棒性,并且在构造伪Zernike矩的过程中采用递归方式,使算法复杂度由原来的(N2p3)降到0(N2p2),’极大的提高了算法的运行效率。
3.提出了一利t能定位篡改区域并且能估计篡改区域旋转角度及缩放因_子的盲检测方法。将图像的空域和频域相结合,对图像进行划分并将每个区域从空域坐标映射为对数极坐标,保证了在空域坐标中经过旋转、缩放的篡改区域,在对数极坐标下仅仅为沿极轴的平移操作。采用PCA技术去除特征中的冗余数据,利用傅里叶变换以及相位相关频谱来对篡改区域进行定位,并首次根据相位相关频谱来估算篡改区域的旋转角度和缩放因子。大量实验结果表明,该方法能够准确的检测出经过旋转、缩放的篡改区域,并且对图像扭曲操作和图像增强操作也有良好的鲁棒性。
4.针对现有的单次JPEG压缩取证技术,首次将混沌理论应用到反取证技术当中,提出了利用混沌噪声来去除JPEG压缩痕迹。首先利用压缩后的DCT系数间的方差来估计JPEG图像未压缩前各个DCT系数子带的分布模型,根据所估计模型,对于各个DCT系数子带,分别采用Logistic映射产生混沌噪声,并将混沌噪声添加到相应子带中。通过大量实验分析表明,通过添加混沌噪声,可以成功的消除JPEG压缩所带来的DCT系数直方图中的“沟壑“现象,并且该方法可以成功的欺骗现有的基于检测JPEG图像块效应的取证技术。比起同类技术,在一定的压缩因子下,该方法可以保证在添加混沌噪声后,图像有较小的视觉失真。
最后,论文对本文工作进行总结,并给出下一步工作中的研究方向。
Nowadays, with the widely use of the computer network、multimedia technology and low cost digital equipment, it is more convenient to access a digital image. However, when people enjoying the entertainment brought by digital images, the powerful image editing software arose that make it easier for those evil people to modify a digital image. Obviously, when the tampered image was used by the news、 judicature and military, it may mislead the public and throw doubt on the authenticity of the multimedia image, the fairness of the judicial evidence and the reliability of the military intelligence. So, the research of the identification technology for digital image is significative not only to rebuild people's confidence in the news media, but also to maintain social fariness and justice.
Based on the projects granted by the National Nature Science Foundation and Key Program of Natural Science Fund of Tianjin, we study on the identification technology for digital image and pay particular attention to the image content detection and anti-forensic technique. The main contributions of this dissertation are as following:
1. A scheme for detecting the tampered regions based on the energy distribution of the DCT coefficients was proposed. Firstly, the image was transformed from spatial domain to frequency domain, then it was split to sub-blocks and each sub-block was represented by the transformed DCT coefficients, according to the nature of the Discrete Cosine Transform, we extracted only four features for each block, which significantly reduce the computational complexity. The experiments show that the proposed scheme could locate the doctored regions accurately, moreover, it was also robust to Gaussian white noise, Gaussian blur and multiple tampered operations.
2. A robust scheme for identifying the doctored areas that underwent rotation was proposed. Through the analysis of the image statistical characteristics, in order to resist the rotation operation, the Pseudo-Zernike moment was used as the image feature. The image was divided into overlapped blocks, and then n-dimensional low order Pseudo-Zernike moments were extracted for each block, which ganrantee the proposed scheme is more robust to noise adding. During the feaure extraction, a recursive method is adopt, which reduce the computional complexity from O(N2p3) to O(N2p2).
3. A passive detection scheme that can not only locate the tampered regions but also estimate the rotation angle and scaling factor was proposed. The image was segmented into a series of small blocks, each block was mapped to the log-polar domain, which can assure the rotated or scaled tampered areas were just represented by the translation along the log-polar coordinate. In the matching step, PCA was applied to make dimension reduction and the tampered regions were located by using the Fourier transform and phase correlation technique, moreover, by using the phase correlation spectrum, the proposed scheme could estimate the rotation or scaling parameter of the tampered areas perfectly. The experimental results show that the proposed scheme is also robust to image distortion and enhancement operation.
4. A new anti-forensic scheme for the single JPEG compression image was proposed. The chaos theory was applied to the anti-forensic method for the first time. Firstly, the original JPEG image was transformed by the discrete cosine transform, and the model for each DCT sub-band was estimated according to the transformed DCT coefficients, then by using the chaos technique and the estimated model, the chaotic noise was added to each DCT sub-band. The experiments show that, by adding chaotic noise, the proposed scheme could erase the "gully" which caused by the JPEG compression perfectly, moreover, the proposed scheme could also deceive the existing forensic method that based on detecting the JPEG block artifact, compared with the competing method, under certain compression factor, the proposed method only introduce little visual distortion when adding chaotic noise.
Finally, the paper makes a summary of the work, and points out the new research direction for the future work.
引文
[1]搜狐新闻.全球极品造假照片(组图)http://roll.sohu.com/20120817/n350862820.shtml, 2012
[2]李立春,张小虎,刘晓春等.“华南虎”照片的摄像测量研究.科技导报,2008,26(1):59-67
[3]新华网.第八届全国摄影金像奖“造假”事件追踪http://news.xinhuanet.com/politics/2 010-01/14/content_12810211.htm,2010
[4]新浪新闻.“青藏铁路为野生动物开辟生命通道”造假图片http://news.sina.com.cn/c/ p/2008-02-16/034914948587.shtml,2008
[5]新浪军事.伊朗试射多枚导弹照片造假(组图)http://mil.news.sina.com.cn/2008-07-11/0823509935.html,2008
[6]人民网.韩克隆之父基因图片造假http://scitech.people.com.cn/GB/1056/3971003.ht ml,2005
[7]Hwang Woo, Suk Ryu, Young June, et al. Evidence of a pluripotent human embryo nic stem cell line derived from a cloned blastocyst. Science,2004,303(5664):166 9-1674
[8]凤凰网,朝鲜军演照片造假http://news.ifeng.com/mil/video/detail_2013_03/28/236222 19_0.shtml,2013
[9]腾讯新闻.赖斯被美媒体妖魔化(组图)http://news.qq.com/a/20051028/000598.htm,2 005-10-28
[10]Farid H. Creating and detecting doctored and virtual images:Implications to the child pornography prevention act. Department of Computer Science, Dartmouth College, TR2004-518.2004
[11]吴琼,李国辉,涂丹等.面向真实性鉴别的数字图像盲取证技术综述.自动化学报,2008,34(12): 1458-1466
[12]周琳娜,张茹,郭云彪.数字图像内容取证,北京:高等教育出版社,201 1:12-33[13]周琳娜.数字图像盲取证技术研究:[博士学位论文].北京:北京邮电大学,2007
[14]Chun-Shien Lu and Liao, H.-Y.M. Structural digital signature for image authentication:an incidental distortion resistant scheme. IEEE Trans on Multimedia,2003,5(2):161-173
[15]Lefebvre, F., Czyz, J.,et al. A robust soft hash algorithm for digital image signature.2003 International Conference on Image Processing,2003:495-498
[16]Barr, J., Bradley, B.,Hannigan, B.T.. Using digital watermarks with image signatures to mitigate the threat of the copy attack. IEEE International Conference on Acoustics, Speech, and Signal Processing,2003:69-72
[17]Fan Li, Tiegang Gao, Yang Qunting. A novel watermarking scheme for copyright protection based on adaptive joint image feature and visual secret sharing. International Journal of Innovative Computing, Information and Control,2011,7(7(A)):3679-3684
[18]Gil-Je Lee, Eun-Jun Yoon, Kee-Young Yoo. A Novel Multiple Digital Watermarking Scheme for the Copyright Protection of Image. The Fourth International Conference on Innovative Computing, Information and Control,2009:756-759
[19]Fan Li,Tiegang Gao.Yang Qunting. A novel zero-watermark copyright authentication scheme based on lifting wavelet and Harris corner detection. Wuhan University Journal of Natural Sciences,2010,15(5):408-414
[20]Wei-Chin Ku, Te-Chih Chou, Hsin-Lung Wu, et al. A fragile watermarking scheme for Image Authentication with Tamper Detection and Localization. The Fourth International Conference on Genetic and Evolutionary Computing,2010:638-641
[21]科技资讯网.Adobe考虑Photoshop防伪工具分辨照片真伪.http: //www.cnetnews.com.cn/2007/0319/382175.shtml
[22]Granty, R.E.J., Aditya, T. S., Madhu, S.S. Survey on passive methods of image tampering detection. International Conference on Communication and Computational Intelligence, 2010:431-436
[23]Babak Mahdian, Stanislav Saic. A bibliography on blind methods for identifying image forgery. Signal Processing:Image Communication,2010,25(6):389-399
[24]Weiqi Luo,Zhenhua Qu,Feng Pan, et al. A survey of passive technology for digital image forensics. Frontiers of Computer Science in China,2007,1(2):166-179
[25]王朔中,吴酋珉.真伪之辨——数字图像的防伪认证.自然杂志,2010,32(5):281-287
[26]Hany Faird. Digital doctoring:how to tell the real from the fake. Significance,2006,3(4): 162-166
[27]B.L.Shivakumar, Lt. Dr. S.Santhosh Baboo. Detecting copy-move forgery in digital images: a survey and analysis of current methods. Global Journal of Computer Science and Technology,2010,10(7):61-65
[28]Sevinc Bayram, Husrev Senca Taha, Nasir Memon. A survey of copy-move forgery detection techniques. IEEE Western New York Image Processing Workshop,2008:538-542
[29]Fridrich J., Soukal D., Lukas J, Detection of Copy-Move forgery in digital images, Proc. Digital Forensic Research Workshop, Cleveland, OH, August 2003
[30]Popescu C, Farid H. Exposing digital forgeries by detecting duplicated image' regions, Technical Report, TR2004-515, Dartmouth College, Computer Science,2004
[31]Weiqi Luo, Jiwu Huang, Guoping Qiu. Robust detection of region duplication forgery in digital image. The 18th International Conference on Pattern Recognition(ICPR),2006: 746-749
[32]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
[33]魏为民,王朔中,唐振军.一类数字图像篡改的被动认证.东南大学学报(自然科学版),2007,37(1):58-61
[34]Guohui Li, Qiong Wu,Tu, D., et al. A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD. IEEE International Conference on Multimedia and Expo,2007:1750-1753
[35]Jing Zhang, Zhanlei Feng, Yuting Su. A new approach for detecting copy-move forgery in digital images. The 11th IEEE Singapore International Conference on Communication Systems,2008:362-366
[36]Bo Xu, Guangjie Liu, Yuewei Dai. A Fast Image Copy-move Forgery Detection method using Phase Correlation. The Fourth International Conference on Multimedia Information Networking and Security,2012:319-322
[37]Babak Mahdian, tanislav Saic. Detection of copy--move forgery using a method based on blur moment invariants. Forensic science international,2007,171(2):180-189
[38]Sevinc Bayram, Husrev Taha Sencar, Nasir Memon. An efficient and robust method for detecting copy-move forgery. IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP),2009:1053-1056
[39]Hailing Huang, Weiqiang Guo, Yu Zhang. Detection of Copy-Move forgery in digital images using SIFT algorithm, Proceedings of Computational Intelligence and Industrial Application,2008:272-276
[40]X. Pan, S. Lyu. Detecting image region duplication using SIFT features. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010:1706-1709
[41]X. Pan, S. Lyu. Region duplication detection using image feature matching. IEEE Trans on Information Forensics and Security,2010,5(4):857-867
[42]X. Pan, S. Lyu. Exposing Doctored Images by Detecting Duplicated and Distorted Regions. Technical Report SUNYACS-09-02, Department of Computer Science, SUNY Albany, November 2009
[43]Q. Wu, S. Wang, and X. Zhang. Detection of image region-duplication with rotation and scaling tolerance. In Proc.2nd International Conference on Computational Collective Intelligence (ICCCI), Kaohsiung, Taiwan, Lecture Notes in Computer Science,2010. Vol. 6421:100-108.
[44]Guangjie Liu, JunwenWang, ShiguoLian, et al. A passive image authentication scheme for detecting region-duplication forgery with rotation. Journal of Network and Computer Applications,2011,34(5):1557-1565
[45]Lin Gao, Tiegang Gao, Yanjun Cao, et al. A new reversible watermarking scheme based on Integer DCT for medical images. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition(ICWAPR),2012:33-37
[46]Guorui Sheng, Tiegang Gao, Yanjun Cao, et al. Robust algorithm for detection of copy-move forgery in digital images based on ridgelet transform. Lecture Notes in Computer Science,2012, Vol.7530:317-323
[47]Farid H, Detecting digital forgeries using bispectral analysis, Massachusetts Institute of technology.Cambridge, MA,1999.
[48]Ng,T.-T., Chang, S.-F., A data set of authentic and spliced image blocks. Tech. Rep. 203-2004-3,Columbia University,2004
[49]Ng, T.-T., Chang, S.-F., Sun, Q. Blind detection of photomontage using higher order statistics. In Proceedings of the 2004 International Symposium on Circuits and Systems(ISCAS'O4),2004:688-691
[50]Fu, D, Shi, Y.Q, Su, W. Detection of image splicing based on hilbert-huang transform and moments of characteristic functions with wavelet decomposition. Lecture Notes in Computer Science,2006:177-187
[51]Chen, W., Shi, Y.Q., Su, W. Image splicing detection using 2-d phase congruency and statistical moments of characteristic function. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series,2007, vol.6505
[52]Shi, Y.Q., Chen, C., Chen. A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on Multimedia & security,2007:51-62
[53]Popescu A.C. and Hany Farid. Exposing digital forgeries by detecting traces of re-sampling. IEEE Transactions on Signal Processing,2005,53(2):758-767
[54]Kirchner M. Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue. Proceedings of the 10th ACM Workshop on Multimedia and Security, 2008:11-20
[55]Nataraj, L, Sarkar A, Manjunath, B.S.Improving Re-sampling Detection By Adding Noise.Proceedings of the SPIE-the International Society For Optical Engineering.2010, vol.7541:754101-754101-11
[56]Kirchner M., Gloe T. On resampling detection in re-compressed images. Proceedings of first IEEE International Workshop on Information Forensics and Security,2009:21-25
[57]D. Hsiao,S. Pei. Detecting digital tampering by blur estimation. Proc. of the 1st International Workshop on Systematic Approaches to Digital Forensic Engineering, Washington, USA,2005:264-278.
[58]Y. Sutcu, B. Coskun, H.T. Sencar, et al. Tamper detection based on regularity of wavelet transform coefficients. IEEE International Conference on Image Processing(ICIP),2007: 397-400
[59]Ben-juan Yang, Ju-xian Zuo, Ben-yong Liu, et, al. Blur Detection in Image Forensics Using Linear Correlation of Pixels. Proceedings of Chinese Conference on Pattern Recognition (CCPR),2010:1-5
[60]周琳娜,王东明,郭云彪等.基于数字图像边缘特性的形态学滤波取证技术.电子学报,2008,36(6):1047-1051
[61]Zhou Linna, Wang Dongming, Guo Yunbiao.et al. Blur Detection of Digital Forgery Using Mathematical Morphology. KES AMSTA2007, Lecture notes in computer science.2007: 990-998
[62]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
[63]王俊文,刘光杰,戴跃伟等.基于非抽样Contourlet变换的图像模糊取证.计算机研究与发展,2009,46(9):1549-1556
[64]Junwen Wang, Guangjie Liu, Bo Xu, et al. Image Forgery Forensics Based on Manual Blurred Edge Detection. The 2010 International Conference on Multimedia Information Networking and Security,2010:907-911
[65]Gang Cao, Yao Zhao, Rongrong Ni, Edge-based Blur Metric for Tamper Detection, Journal of Information Hiding and Multimedia Signal Processing,2010,1(1):20-27
[66]Micah K. Johnson, Hany Farid. Exposing digital forgeries in complex lighting environments. IEEE Trans on Information Forensics and Security,2007,3(2):450-461
[67]Micah K. Johnson, Hany Farid. Exposing digital forgeries by detecting inconsistencies in lighting. Proceedings of the 7th workshop on Multimedia and security,2005:1-10
[68]Micah K. Johnson, Hany Farid. Exposing digital forgeries through specular highlights on the eye. In:The 9th International Workshop on Information Hiding,Saint Malo, France, 2007:311-325
[69]陈海鹏,申铉京,吕颖达等.基于Lambert光照模型的图像真伪盲鉴别算法.计算机研究与发展,2011,48(7):1237-1245
[70]Mahajan D., Ramamoorthi R., Curless, B. A theory of frequency domain invariants: Spherical harmonic identities for BRDF/lighting transfer and image consistency. IEEE Trans on Pattern Analysis and Machine Intelligence,2008,30(2):197-213
[71]Yi-Lei Chen, Chiou-Ting Hsu. Detecting doubly compressed images based on quantization noise model and image restoration. IEEE International Workshop on Multimedia Signal Processing,2009:1-6
[72]Fan Z., De Queiroz Ricardo. Maximum likelihood estimation of JPEG quantization table in the identification of bitmap compression history. Proceedings of International Conference on Image Processing,2000:948-951
[73]Fan Z., De Queiroz Ricardo. Identification of bitmap compression history:JPEG detection and quantizer estimation. IEEE Trans on Image Processing,2003,12(2):230-235
[74]H. Farid. Exposing digital forgeries from jpeg ghosts. IEEE Trans on Information Forensics and Security,2009,4(1):154-160
[75]Lukas J., Fridrich J. Estimation of primary quantization matrix in double compressed JPEG images. Processings of Digital Forensic Research Workshop,Cleveland,OH,USA,2003: 5-8
[76]Pevny T., Fridrich J. Detection of Double-Compression in JPEG Images for Applications in Steganography. IEEE Trans on Information Forensics and Security,2008,3(2):247-258
[77]A. C. Popescu, H. Farid. Statistical tools for digital forensics. In Proceedings of the 6th Information Hiding Workshop,2004
[78]Weiqi Luo, Jiwu Huang, Guoping Qiu. A novel method for block size forensics based on morphological operations. The 7th International Workshop on Digital Watermarking,2009: 229-239
[79]Weiqi Luo, Jiwu Huang, Guoping Qiu. A novel method for detecting cropped and recompressed image block. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),2007:217-220
[80]Junfeng He, Zhouchen Lin, Lifeng Wang,et al. Detecting doctored JPEG images via DCT coefficient analysis. Proeeedings of 9th European Conference on Computer Vision,2006: 423-435
[81]Ting Zhang, Wang Rangding. Doctored JPEG image detection based on double compression features analysis. ISECS International Colloquium on Computing, Communication, Control, and Management,2009:76-80
[82]Sankar G., Zhao V., Yee-Hong Yang. Feature based classification of computer graphics and real images. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),2009:1513-1516
[83]Dehnie S., Sencar T., Memon N. Digital Image Forensics for Identifying Computer Generated and Digital Camera Images. IEEE International Conference on Image Processing,2006:2313-2316
[84]TT Ng, SF Chang, J Hsu, et al. Physics-motivated features for distinguishing photographic images and computer graphics. Proceedings of the 13th annual ACM international conference on Multimedia,2005:239-248
[85]Tian-Tsong Ng, Shih-Fu Chang. An online system for classifying computer graphics images from natural photographs. In:SPIE Electronic Imaging,2006
[86]Tian-Tsong Ng, Shih-Fu Chang, Mao-Pei Tsui. Lessons Learned from Online Classification of Photo-Realistic Computer Graphics and Photographs. IEEE Workshop on Signal Processing Applications for Public Security and Forensics,2007:1-6
[87]Dirik A.E., Bayram S., Sencar H. T., et al. New Features to Identify Computer Generated Images. IEEE International Conference on Image Processing,2007:433-436
[88]Leykin A., Cutzu F. Differences of edge properties in photographs and paintings. International Conference on Image Processing,2003:541-544
[89]Cutzu F., Hammoud R., Leykin A., Distinguishing paintings from photographs. Computer Vision and Image Understanding,2005,100(3):249-273
[90]S. Lyu, Farid H. How realistic is photorealistic?. IEEE Trans on Signal Processing,2005, 53(2):845-850
[91]Wen Chen, Shi, Y.Q., Guorong Xuan. Identifying Computer Graphics using HSV Color Model and Statistical Moments of Characteristic Functions. IEEE International Conference on Multimedia and Expo,2007:1123-1126
[92]Patchara Sutthiwan, Jingyu Ye, Yun Q. Shi. An enhanced statistical approach to identifying photorealistic images. Proceedings of the 8th International Workshop, IWDW 2009, Guildford, UK, August,2009:323-335
[93]Harris R, Arriving at an anti-forensics consensus:examining how to define and control the anti-forensics problem, Digital Investigation,2006:44-49
[94]Wang J, Cha B, Cho S, et al. Understanding Benford's law and its vulnerability in image forensics. IEEE International Conference on Multimedia and Expo,2009:1568-1571
[95]Kirchner M, Bhme R. Hiding traces of resampling in digital images. IEEE Trans on Information Forensics and Security,2008,3(4):582-592
[96]Stamm M.C, Liu, K. J. R. Anti-forensics of digital image compression. IEEE Trans onInformation Forensics and Security,2011,6(3):1050-1065
[97]Matthew C. Stamm, K. J. Ray Liu. Wavelet-based image compression anti-forensics. Proceedings of 2010 IEEE 17th International Conference on Image Processing,2010: 1737-1740
[98]Gang Cao, Yao Zhao, Rongrong Ni, et al. Anti-forensics of contrast enhancement in digital images. Proceedings of the 12th ACM workshop on Multimedia and security,2010:25-34
[99]http://www.ee.columbia.edu/ln/dvmm/trustfoto/
[100]阮秋琦,阮宇智等译.数字图像处理(第二版).北京:电子工业出版社,2006
[101]Yanping Huang, WeiLu, Wei Sun, et al. Improved DCT-based detection of copy-move forgery in images. Forensic science international,2011,206(1):178-184
[102]http://www.ee.columbia.edu/ln/dvmm/newDownloads.htm
[103]http://rOk.us/graphics/kodak/
[104]王俊文,刘光杰,张湛等.图像区域复制篡改快速鲁棒取证.自动化学报,2009,35(12):1487-1495
[105]Haddadnia J., Ahmadi M., Faez K. An efficient method for recognition of human faces using higher orders Pseudo Zernike Moment Invariant. IEEE International Conference on the Fifth Automatic Face and Gesture Recognition,2002:330-335
[106]Yongqing Xin, Liao, S., Pawlak, M. Geometrically robust image watermarking via pseudo-Zernike moments. Canadian Conference on Electrical and Computer Engineering, 2004:939-942
[107]Hyung Shin Kim, Heung-Kyu Lee. Invariant image watermark using Zernike moments. IEEE Trans on Circuits and Systems for Video Technology,2003,13(8):766-775
[108]李雷达,郭宝龙,刘雅宁.基于伪Zernike矩的抗几何攻击图像水印.光电子·激光,2007,18(2):231-235
[109]叶斌,彭嘉雄.伪Zernike矩不变性分析及其改进研究.中国图象图形学报,2003,8A(3):246-252
[110]Javad Haddadnia, Majid Ahmadi, Karim Faez. An efficient feature extraction method with pseudo-Zernike moment in RBF neural network-based human face recognition system. EURASIP journal on applied signal processing,2003,2003(9):890-901
[111]G. A. Papakostas, Y. S. Boutalis, D. A. Karras and B. G. Mertzios. Efficient computation of Zernike and Pseudo-Zernike moments for pattern classification applications. Pattern Recognition and Image Analysis,2010,20(1):56-64
[112]Hao-Chiang Hsu, Min-Shi Wang. Detection of copy-move forgery image using Gabor descriptor. IEEE International Conference on Anti-Counterfeiting, Security and Identification (ASID),2012:1-4
[113]Myna.A.N., M.G.Venkateshmurthy, C.G.Patil. Detection of region duplication forgery in digital images using wavelets and log-polar mapping. International Conference on Computational Intelligence and Multimedia Applications,2007:371-377
[114]Xu Bo, Wang Junwen, Liu Guangjie, et al. Image copy-move forgery detection based on SURF.2010 International Conference on Multimedia Information Networking and Security (MINES),2010:889-892
[115]汪洋,黄小红,陈曾平.基于对数极坐标变换的目标识别算法.雷达科学与技术,2009,7(2):111-114
[116]Ridzon R., evicky D.. Log-polar mapping in robust digital image watermarking. IEEE International Conference on Radioelektronika,2007:1-4
[117]李忠新,茅耀斌,王执铨.基于对数极坐标映射的图像拼接方法.中国图象图形学报,2005,10(1):59-63
[118]余博,郭雷,赵天云.基于对数极坐标变换的灰度投影稳像算法.计算机应用,2008,28(12):3216-3218
[119]D. Zheng, J. Zhao, A. El Saddik. RST-invariant digital image watermarking based on log-polar mapping and phase correlation, IEEE Trans on Circuits and Systems for Video Technology,2003,13(8):753-765
[120]K. I. Kim, M. O. Franz, B. Scholkopflterative kernel principal component analysis for image modeling, IEEE Trans on Pattern Analysis and Machine Intelligence,2005,27(9): 1351-366
[121]Ting Zhang, Rangding Wang. Doctored JPEG image detection based on double compression features analysis. ISECS International Colloquium on Computing, Communication, Control, and Management,2009:76-80
[122]Junwen Wang, Guangjie Liu, Yuewe Dai, et al. Detecting JPEG image forgery based on double compression. Journal of Systems Engineering and Electronics,2009,20(5):1096-1103
[123]郑二功,平西建.针对一类JPEG图像伪造的被动盲取证.电子与信息学报,2010,32(2):394-399
[124]Weiqi Luo, Jiwu Huang, Guoping Qiu. JPEG error analysis and its applications to digital image forensics. IEEE Trans on Information Forensics and Security,2010,480-491
[125]E.Kee, M.K.Johnson, H.Farid. Digital image authentication from JPEG headers. IEEE Trans on Information Forensics and Security,2011,6(3):1066-1075
[126]W. Li, Y. Yuan, N. Yu. Passive detection of doctored jpeg image via block artifact grid extraction. Signal Process,2009,89(9):1821-1829
[127]Matthew C. Stamm, Steven K. Tjoa, W. Sabrina Lin,et al. Undetectable image tampering through JPEG compression anti-forensics.The 17th IEEE International Conference on Image Processing (ICIP),2010:2109-2112
[128]Matthew C. Stamm, Steven K. Tjoa, W. Sabrina Lin,et al. Anti-forensics of JPEG compression. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010:1694-1697
[129]廖晓峰,肖迪,陈勇等.混沌密码学原理及应用.北京:科学出版社,2009
[130]包浩明.混沌理论在报名通信系统的应用研究:[博士学位论文].大连:大连海事大学,2011
[131]Pareek NK, Patidar Vinod, Sud KK. Image encryption using chaotic logistic map. Image and Vision Computing,2006,24(9):926-934
[132]Tiegang Gao,Zengqiang Chen. A new image encryption algorithm based on hyper-chaos. Physics Letters A,2008,372(4):394-400
[133]高铁杠,谭琳,曹雁军.基于Hash函数和汉明码的半脆弱水印算法.光电子·激光,2012,23(9):1808-1804
[134]E. Lam, J. Goodman. A mathematical analysis of the dct coefficient distributions for images. IEEE Trans Image Processing.2000,9(10):1661-1666
[135]V. Dwivedi. JPEG Image Compression and Decompression with Modeling of DCT Coefficients on the Texas Instrument Video Processing Board TMS320DM6437. PhD thesis, California State University, Sacramento,2010
[136]R. L. de Queiroz. Processing jpeg-compressed images and documents. IEEE Trans Image Processing,1998,7(12):1661-1672
[137]P. Sallee, Matlab JPEG Toolbox:http://www.philsallee.com/jpegtbx/index.html
[138]G. Schaefe, M. Stich. Ucid-an uncompressed color image database. SPIE, Storage and Retrieval Methods and Applications for Multimedia,2004,:472-480