全景图构建及信息加密在公共安全中的研究与应用
|
摘要
面对高级别风险区域日益严峻的安全管控形势，全景安检及图像加密技术成为迫切需求。“全景图构建及信息加密在公共安全中的研究与应用”是将全景图构建理论与技术、信息安全理论与技术相结合，研究安检图像碎片全景构建、图像加密的理论与应用，具有重要的理论意义和实用价值。
首先，安检全景图构建主要包括图像预处理、图像配准和图像融合，图像配准是全景图构建的基础和关键。分析安检图像的采集方法及安检碎片的特点，对图像预处理、图形配准、图像融合问题作了深入研究；针对SIFT描述子构建过程复杂、描述向量维度高、需要计算特征点附近邻域图像主方向等问题，本文提出一种改进的SIFT算法，利用圆形的旋转不变性把特征描述子的维数从128维降为48维，安检图像及标准测试图库比较试验可知，改进算法提高了特征点生成速度，减少了特征匹配的运算量；在特征点匹配方面，本文提出一种基于模长的匹配算法，步骤为限幅、归一化、计算相似匹配对模长的差值，通过限定差值确定图像间的匹配对，相比SIFT常用的遍历搜索NN算法，算法复杂度低，匹配效率高；在图像融合方面，本文提出一种基于亮度调制的非线性加权融合算法，有效抑制了融合鬼影及拼缝的产生。
其次，阐述了图像加密环节的重要性及性能指标，分析了混沌基础理论、密码学基础理，并进行了两方面研究：(1)对一类传统的Logistic映射的混沌加密算法分析。分析算法原理，针对安检图像设计算法步骤，并进行算法安全性及算法效率测试，实验分析可知，该算法局部性能较好，但总体效果不佳，不适合安检图像加密。(2)提出一种多混沌系统图像置乱扩散加密算法。该加密算法分别通过Logistic映射和Kent映射产生置乱和扩散控制参数，并分别以Arnold映射和模加的方式实现图像置乱和灰度扩散。试验结果表明，改进的多混沌加密算法，密钥空间大且密钥敏感性强，能有效的抵挡穷举分析攻击；加密后像素灰度分布均匀、相邻像素点相关性很弱，能有效抵挡统计分析攻击；像素变化比率和灰度值平均改变程度在加密轮次大于等于2时同时达到理想值，抗差分攻击能力很强；较传统加密算法，加密效率高，实时性强。
最后，针对公共安全非接触式安检中全景安检及图像加密问题，结合本文提出算法及方法，进行“全景安检及图像加密系统”设计，给出智能车非接触式采集方案，在此基础上实现了全景安检及图像加密，并进行了应用测试。设计的试验样机，具有重要的理论意义和应用前景。
Due to the security situation been more and more serious, especially in High-levelrisk areas, panoramic and encryption technology has become an urgent requirement.Dissertation “Research on Panoramic&Information Encryption and its Application inPublic Security”, panorama theory and information security technology are combined,and the security scene mosaic and the image encryption theories are used in securityimage fragments. It has important theoretical significance and practical value.
Firstly, the key steps in Panorama mosaic are mainly image preprocessing, imageregistration and image fusion. The second step is the base and key of mosaictechnology. The paper studies above steps about special application in security imagefragments. SIFT is a kind of feature point registration algorithm. It remains invariantfor scale transformation, rotating, brightness change, and good stability. So it iswidely used in image registration. But the feature description vectors in SIFT is128dimensions, which causes large computation complexity of corresponding matchingalgorithm has and lower efficiency. In this paper, an improved SIFT algorithm isproposed to reduce the dimension from128d to48d by using circular rotationinvariance. It improves the extraction speed of feature point and reduces thecomputation complexity of feature matching. To match the feature point, a matchingalgorithm based on modulus is used, which includes limiter, normalization, andmodulus calculation. And then the difference with the similarity matching modulus iscalculated so as to determine matching pairs through limiting difference. Comparedwith normal NN algorithm, the computation complexity of this method is decreasedand matching efficiency is improved. To Image fusion overlap region, a nonlinearweighted fusion algorithm is used, which can effectively eliminate the patchwork andghosting and ensures consistency of image brightness.
Secondly, the importance and the performance indexes of image encryption areshown. The fundamental knowledge of cryptography and chaos theory as well aschaotic cryptography and its development are analyzed. Then the following two kindsof encryption algorithm are compared:(1) A traditional image encryption algorithmbased on Logistic chaotic system is given. By analysis algorithm principle, designalgorithm steps and test the safety and efficiency in security image, the analysis andtests show that the local performance is good whereas the overall effect is not suitable for encryption of the system.(2) Image encryption algorithm of scrambling anddiffusion based on multiple chaotic systems is proposed. The control parameters ofscrambling and diffusion are produced by Logistic and Kent mapping. The scramblingand diffusion of the gray value are realized by Arnold mapping and the modularaddition. The tests results indicate that this algorithm meets the basic security andreal-time requirements of the system. The experiment results show that, the improvedmulti-chaotic encryption algorithms has large key space and key sensitivity, whichcan effectively resist the exhaustive analysis of the attack; encrypted pixel graydistribution, weak correlation of adjacent pixels can effectively statistical analysisresist attack. Alternated2generations, the NPCR and UACI can achieve the desiredfor anti-differential attacks. Compared with the traditional encryption algorithms, theimproved multi-chaotic encryption algorithm has high encryption efficiency.
At last, in order to solve the problems that panoramic inspection and imageencryption application in security protection,"panoramic image of security andencryption systems" was designed. It included hardware acquisition of smart car andpanoramic image of security and encryption software. The application testing showthat, experimental prototype have important theoretical significance and applicationvalue.
首先，安检全景图构建主要包括图像预处理、图像配准和图像融合，图像配准是全景图构建的基础和关键。分析安检图像的采集方法及安检碎片的特点，对图像预处理、图形配准、图像融合问题作了深入研究；针对SIFT描述子构建过程复杂、描述向量维度高、需要计算特征点附近邻域图像主方向等问题，本文提出一种改进的SIFT算法，利用圆形的旋转不变性把特征描述子的维数从128维降为48维，安检图像及标准测试图库比较试验可知，改进算法提高了特征点生成速度，减少了特征匹配的运算量；在特征点匹配方面，本文提出一种基于模长的匹配算法，步骤为限幅、归一化、计算相似匹配对模长的差值，通过限定差值确定图像间的匹配对，相比SIFT常用的遍历搜索NN算法，算法复杂度低，匹配效率高；在图像融合方面，本文提出一种基于亮度调制的非线性加权融合算法，有效抑制了融合鬼影及拼缝的产生。
其次，阐述了图像加密环节的重要性及性能指标，分析了混沌基础理论、密码学基础理，并进行了两方面研究：(1)对一类传统的Logistic映射的混沌加密算法分析。分析算法原理，针对安检图像设计算法步骤，并进行算法安全性及算法效率测试，实验分析可知，该算法局部性能较好，但总体效果不佳，不适合安检图像加密。(2)提出一种多混沌系统图像置乱扩散加密算法。该加密算法分别通过Logistic映射和Kent映射产生置乱和扩散控制参数，并分别以Arnold映射和模加的方式实现图像置乱和灰度扩散。试验结果表明，改进的多混沌加密算法，密钥空间大且密钥敏感性强，能有效的抵挡穷举分析攻击；加密后像素灰度分布均匀、相邻像素点相关性很弱，能有效抵挡统计分析攻击；像素变化比率和灰度值平均改变程度在加密轮次大于等于2时同时达到理想值，抗差分攻击能力很强；较传统加密算法，加密效率高，实时性强。
最后，针对公共安全非接触式安检中全景安检及图像加密问题，结合本文提出算法及方法，进行“全景安检及图像加密系统”设计，给出智能车非接触式采集方案，在此基础上实现了全景安检及图像加密，并进行了应用测试。设计的试验样机，具有重要的理论意义和应用前景。
Due to the security situation been more and more serious, especially in High-levelrisk areas, panoramic and encryption technology has become an urgent requirement.Dissertation “Research on Panoramic&Information Encryption and its Application inPublic Security”, panorama theory and information security technology are combined,and the security scene mosaic and the image encryption theories are used in securityimage fragments. It has important theoretical significance and practical value.
Firstly, the key steps in Panorama mosaic are mainly image preprocessing, imageregistration and image fusion. The second step is the base and key of mosaictechnology. The paper studies above steps about special application in security imagefragments. SIFT is a kind of feature point registration algorithm. It remains invariantfor scale transformation, rotating, brightness change, and good stability. So it iswidely used in image registration. But the feature description vectors in SIFT is128dimensions, which causes large computation complexity of corresponding matchingalgorithm has and lower efficiency. In this paper, an improved SIFT algorithm isproposed to reduce the dimension from128d to48d by using circular rotationinvariance. It improves the extraction speed of feature point and reduces thecomputation complexity of feature matching. To match the feature point, a matchingalgorithm based on modulus is used, which includes limiter, normalization, andmodulus calculation. And then the difference with the similarity matching modulus iscalculated so as to determine matching pairs through limiting difference. Comparedwith normal NN algorithm, the computation complexity of this method is decreasedand matching efficiency is improved. To Image fusion overlap region, a nonlinearweighted fusion algorithm is used, which can effectively eliminate the patchwork andghosting and ensures consistency of image brightness.
Secondly, the importance and the performance indexes of image encryption areshown. The fundamental knowledge of cryptography and chaos theory as well aschaotic cryptography and its development are analyzed. Then the following two kindsof encryption algorithm are compared:(1) A traditional image encryption algorithmbased on Logistic chaotic system is given. By analysis algorithm principle, designalgorithm steps and test the safety and efficiency in security image, the analysis andtests show that the local performance is good whereas the overall effect is not suitable for encryption of the system.(2) Image encryption algorithm of scrambling anddiffusion based on multiple chaotic systems is proposed. The control parameters ofscrambling and diffusion are produced by Logistic and Kent mapping. The scramblingand diffusion of the gray value are realized by Arnold mapping and the modularaddition. The tests results indicate that this algorithm meets the basic security andreal-time requirements of the system. The experiment results show that, the improvedmulti-chaotic encryption algorithms has large key space and key sensitivity, whichcan effectively resist the exhaustive analysis of the attack; encrypted pixel graydistribution, weak correlation of adjacent pixels can effectively statistical analysisresist attack. Alternated2generations, the NPCR and UACI can achieve the desiredfor anti-differential attacks. Compared with the traditional encryption algorithms, theimproved multi-chaotic encryption algorithm has high encryption efficiency.
At last, in order to solve the problems that panoramic inspection and imageencryption application in security protection,"panoramic image of security andencryption systems" was designed. It included hardware acquisition of smart car andpanoramic image of security and encryption software. The application testing showthat, experimental prototype have important theoretical significance and applicationvalue.
引文
[1] Alliney S., Morandi C.. Digital image registration using projections[J]. IEEE TransactiononPattern Analysis and Machine Intelligence,1986,8(2):222-233
[2] R Szeliski. Image Alignment and Stitching: A Tutorial[R]. Richard Szeliski Preliminarydraft,January26,2005，Technical Report，MSR-TR-2004-92.
[3]方俊伟，翟超，金熠.相位相关法实现图像拼接的仿真和优化[J].计算机应用与软件,2008,25(1):207-208
[4]王俊杰，刘家茂，胡运发等.图像拼接技术[J]．计算机科学，2003，30（6）：141-144.
[5]蔡丽欢，廖英豪，郭东辉．图像拼接方法及其关键技术研究[J]．计算机技术与发展，2008，18（3）：1-4.
[6]李弼程，彭天强，彭波.智能图像处理技术[M].北京：电子工业出版社，2004.
[7]刘卫光.图像信息融合与识别[M].北京：电子工业出版社，2008.
[8] Zhang Z., Deriche R., Faugeras O., Luong Q.T., A robust technique for matching twouncalibrated images through the recovery of the unknown epipolar geometry[J].ArtificialIntelligence Journal,1995,78:87-119
[9] Szeliski R.. Video mosaics for virtual environments[J]. IEEE Computer Graphics andApplications,1996,16(2):16-20
[10] McMillan L., Bishop G.. Plenoptic modeling: An image-based renderingsystem[C].Computer Graphics (SIGGRAPH'95),1995:39–46
[11] Zokai S., Wolberg G.. Image Registration Using Log-Polar Mappings for Recovery ofLarge-Scale Similarity and Projective Transformations[J]. IEEE Transactions on ImageProcessing,2005,14(10):1422-1434
[12] Chen S.E.. QuickTime VR-an image-based approach to virtual environment navigation.[C].Computer Graphics (SIGGRAPH'95),1995:29–38
[13]罗诗途,王艳玲,张玘，等.车载图像跟踪系统中电子稳像算法的研究[J]．光学精密工程，2005，13（1）：95-103.
[14] Alexandrov V V, Gorsky N D, Mysko S N. A fast technique for recursive scene matchingusing pyramids[J]. Pattern Recognition Letters,1985,3:413-419.
[15]张立华，徐文立.基于遗传算法的点模式匹配方法[J].电子学报，2000，28（10）：36-40.
[16] Li R X, Zeng B, Liou M L. A new three-step search algorithm for block motionestimation[J].IEEE Transactions on Circuits&Systems for Video Technology,1994，4（4）：438-442.
[17] Po L. M., Ma W. Ch, A novel four-step search algorithm for fast block motion estimation[J].IEEE Transactions on Circuits&Systems for Video Technology,1996，6（3）：313-317.
[18] Zhu S, Ma K K. A new diamond search algorithm for fast block-matching motionestimation[J]. IEEE Trans Image Processing,2000,9(2):287-290.
[19] C. Zhu, X. Lin, L. P. Chau, et al. A novel hexagon-based search algorithm for fast blockmotion estimation[C].Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, May2001.
[20] Kuglin C D, Hines D D. The phase correlation image alignment method[C]. IEEEConference on Cybernetics and Society,1975:163-175.
[21]张静，胡志萍，刘志泰，等.基于轮廓相位相关的图像自动拼接[J]．大连理工大学学报，2005，45（1）：68-74.
[22] Bailey J G, Messenr R A. Log-polar Mapping as a preprocessing stage for an image trackingsystem [J]. Intelligent Robots and Computer Vision,1988,(7):223-227.
[23] Lim F L, West G A, Venkatesh S. Use of log polar space for foveation and featurerecognition[C].IEEE:Proc-Vis. Image Signal Process,1997,144(6)
[24]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[J]．中国图像图形学报，2005，10（1）：59-63.
[25]安晓东.基于Fourier-Melllin变换的宽视场监控图像合成技术[J].中北大学学报（自然科学版），2007，28（2）：181-183.
[26]李晓明，赵训坡，郑链，等.基于Fourier-Melllin变换的图像配准方法及应用拓展[J].计算机学报，2006，29（3）：466-472.
[27] Chen Q S, Defrise M, Deconinck F.Symmetric Phase-only matched filtering ofFourier-Mellin transforms for image registration and recognition[J].IEEE Transactions onPattern Analysis and Machine Intelligence,1994，16（12）：1156-1168.
[28] Ji Zhou, Jiaoying shi. A robust algorithm for feature point matching[J].Computers&Graphics,2002,26:429-436.
[29] Dai X L, Khorram S. A feature-based image registration algorithm using improvedchain-code representation combined with invariant moments[J]. IEEE Trans. on Geoscienceand Remote Sensing,1999,37(5):2351-2362.
[30] Mallat S. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation[J].IEEE Trans. on Pattern Anal. Mach. Intell.,1989,11(7):674-693.
[31] Tian G Y, Gledhill Duke， Taylor D. Comprehensive interest points based imagingmosaic.Pattern Recognition Letters,2003,24:1171–1179.
[32] Gao G D, Jia K B. A New Image Mosaics Algorithm Based on Feature PointsMatching[C].IEEE,2007,0-7695-2882-1/07.
[33] Yang ZL, Guo B L. Image mosaic based on SIFT[C]. International Conference on IntelligentInformation Hiding and Multimedia Signal Processing,2008,1422-1425.
[34] M Brown, D G Lowe. Recognising Panoramas. Proceedings of IEEE InternationalConference on Computer Vision,2003,1218-1225.
[35]王君本，卢选民，贺兆.一种基于快速鲁棒特性的图像匹配算法[J].计算机科学与工程，2011,33(2):112-117.
[36]刘恩鹏.仪表图像识别读表技术研究与系统实现[D].徐州：中国矿业大学，2012.
[37]刘卫光.图像信息融合与识别[M].北京：电子工业出版社，2008.
[38]孙兆林. MATLAB6.x图像处理[M].北京：清华大学出版社，2002.
[39] L. Brown. A survey of image registration techniques[J]. ACM Computer Surveys,1992,24(4):325-376.
[40]李国胜，林宗坚，任超锋等.相机校验中标志点的自动提取[J].测绘科学，2010,35(6):161-163.
[41]曹俊.基于虚拟点的可见光和SAR图像配准研究[J].光电工程，2009,36(11):79-84.
[42]王静.基于SIFT和角点检测的自动图像配准方法研究[D].武汉：华中科技大学，2010.
[43]周鹏，谭勇，徐守时.基于角点检测图像配准的一种新算法[J].中国科学技术大学学报，2002,32(4):455-461.
[44]那彦，焦李成.基于多分辨率分析理论的图像融合方法[M].西安：西安电子科技大学出版社，2007.
[45]刘卫光.图像信息融合与识别[M].北京：电子工业出版社，2008.
[46] J. Canny. A computational approach to edge detection[J]. IEEE Transactions on PatternAnalysis and Machine Intelligence,1986,8(6):679-698.
[47]李实英，杨高波.特征提取与图像处理（第二版）[M].北京：电子工业出版社，2011.
[48]李俊山，杨威，张雄美.红外图像处理、分析与融合[M].北京：科学出版社，2009.
[49] S. M. Smith and J. M. Brady. SUSAN-a new approach to low level image processing[J].International Journal of Computer Vision,1997,23(1):45-78.
[50]夏良正.数字图像处理编程入门[M].江苏：东南大学出版社，1999.
[51]阮秋琦，阮志宇.数字图像处理（冈萨雷斯）第二版[M].北京：电子工业出版社，2008.
[52] D. Lowe. Object Recognition from Local Scale-Invariant Features[C]. InternationalConference on Computer Vision,1999,1150-1157.
[53] D. Lowe. Distictive Image Features from Scale-Invariant Keypoints[J]. International Journalof Computer Vision,2004,60(2):91-110
[54]王山虎,尤红建,付琨.基于大尺度双边SIFT的SAR图像同名点自动提取方法[J].电子与信息学报,2012,34(2):287-293.
[55]陈抒瑢,李勃,董蓉,陈启美. Contourlet-SIFT特征匹配算法[J].电子与信息学报,2012,35(5):1215-1221.
[56]唐朝伟,肖健,邵艳清,苗光胜.一种改进的SIFT描述子及其性能分析[J].武汉大学学报,2012,37(1):11-16.
[57]胡珂立,谷宇章,王营冠,邹方圆,金锋.基于分水岭分割和尺度不变特征点的多目标全自主跟踪算法[J].电子与信息学报,2012,34(8):1827-1832.
[58] Huo Ju, Yang Ning, Cao Mao-yong, and Yang Ming. A reliable algorithm for image matchingbased on SIFT[J]. Jounal of Harbin Institute of Technology,2012,19(4):90-95.
[59] Mikolajczyk K and Schmid C. A performance evaluation of local descriptors[J]. IEEETransactions on Pattern Analysis an Machine Intelligences,2005,27(10):1615-1630.
[60] Mikolajczyk K, Matas J. Improving descriptors for fast tree matching by optimal linearprojection[C]. In: Proceedings of the11th IEEE International Conference on ComputerVision. Rio de Janeiro, Brazil: IEEE,2007,1-8
[61] Klare B, Li Z F, Jain A K. Matching forensic sketches to mug shot photos[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2011,33(3):639-646
[62] Yan K, Sukthankar R. PCA-SIFT: A More Distinctive Representation for Local ImageDescriptors[C]. In: Proceedings of the Conference on Computer Vision and PatternRecognition. Washington, USA:IEEE,2004,1,511-517.
[63] Bay H, Tuytelaars T, and Gool Van J L. SURF: Speeded Up Robust Features[C]. EuropeanConference on Computer Vision, Graz, Austria, May,2006:404-417.
[64]曾峦,顾大龙.一种基于扇形区域分割的SIFT特征描述符[J].自动化学报,2012,38(9):1513-1519.
[65]张良,王海丽,吴仁彪.基于改进局部不变特征的兴趣点匹配[J].电子与信息学报.2009,31(11):2620-2625.
[66] Kaiyang Liao, Guizhong Liu, and Youshi Hui. An improvement to the SIFT descriptor forimage representation and matching[J]. Pattern Recognition Letters,2013(34):1211-1220.
[67] C. Harris and M. Stephens. A Combined Corner and Edge Detector[C]. In The4thAlveyVision Conference Manchester UK:147-151.
[68]王民钢，王超，樊英平.基于Harris角点检测改进算法[J].计算机仿真，2012,11:294-299.
[69] S. M. Smith, J. M. Brandy. SUSAN-A New Approach to Low Level Image Processing[J].International Journal of Computer Vision,1997,23(1):45-87.
[70]周乐.基于SUSAN检测算子的二维条码定位方法[J].微型机与应用，2012,31(9):35-38.
[71] T, Lindeberg. Scale-space theory: A basic tool for analyzing structures at different scales.Journal of Applied Statistics，1994，21(2):224-270.
[72]蔡勇，刘学慧，吴恩华.基于图像绘制的虚拟显示系统环境[J].软件学报，1997,8(10):721-728.
[73]杨萍.基于图像拼接的车载全视角观测器研究与实现[D].北京：北京工业大学，2011.
[74] K. Mikolajczyk. Detection of local features invariant to affine transformations:[ph.D.thesis].Institut National Polytechnique de Grenoble, France,2002.
[75]王永明，王贵锦.图像局部不变性特征与描述[M].北京：国防工业出版社，2010.
[76] Schmid.C, Mohr.R, Bauckhage.C. Eva1uation of Interest point Detectors[J]. Int. Joumal ofComPuter Vision,2000,37(2):151-172.
[77]李欢欢，黄山，张洪斌.基于Harris和SIFT算法的自动图像拼接[J].计算机工程与科学，2012,11:104-108.
[78]党建武，宗岩，王阳萍.基于SIFT特征检测的图像拼接优化算法研究[J].计算机应用研究，2012,29(1):329-333.
[79] Johnson A, Hebert M. Using spin images for efficient object recognition in cluttered3dscenes[J]. IEEE Trans. PAMI,1999,21(5).
[80]朱志文,沈占峰,骆剑承.改进SIFT点特征的并行遥感影像配准[J].遥感学报,2011,15(5):1032-1039.
[81] Friedman.J.H, Bentley J.L, Finkel.R.A..Analgorithm for finding best matches in logarithmicexpected time[J].ACM Transactions on Mathematical Software,1977,3(3):209-226.
[82] Jeffrey S. Beis, D. G. Lowe. Shape indexing using approximate nearest neighbor search inhigh-dimensional spaces[C]. In Conference on Computer Vision and Pattern Recognition,Puerto Rico,1997,1000-1006.
[83] M.A. Fischler, R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting withApplications to Image Analysis and Automated Cartography[J]. Comm. Of theACM,1981:381-395.
[84]周乐.基于SUSAN检测算子的二维条码定位方法[J].微型机与应用，2012,31(9):35-38.
[85] D. G. Feng, X. Y. Wang. Progress and Prospect of some Fundamental Research onInformation Security in China[J]. J. Comput. Sci. Technol.,2006,21(5):740–755.
[86] P. Moulin, R. Koetter. Data Hiding Codes[J]. P. IEEE,2008,12:2083–2126.
[87] C. E. Shannon. Communication theory of secrecy systems[J]. Bell Systems Technical Journal,1949,28(4):656-715.
[88] Matthews R. On the derivation of a “chaotic” encryption algorithm[J]. Cryptologia,1989,13:29-36.
[89] Marco Tomassini, Malthieu Perrenoud. Cryptography with cellular automata[J]. Applied SoftComputing,2001,1(2):151-160.
[90] Kennedy J, Eberhart R. Particle swarm optimization[C]. IEEE International Conference onNeural Networks, Perth, Australia,1995,4:1942-1948.
[91] Guo Chen, Yong Chen, Xiaofeng Liao. An extended method for obtaining S-boxes based onthree-dimensional chaotic Baker maps[J]. Chaos, Solitons and Fractals,2007,31:571–579.
[92] Di Xiao, Xiaofeng Liao, K..W. Wong. An efficient entire chaos-based scheme for deniableau-thentication[J]. Chaos, Solitons and Fractals,2005,23(4):1327–1331.
[93] Linhua Zhang, Xiaofeng Liao, Xuebing Wang. An image encryption approach based onchaotic maps[J]. Chaos, Solitons&Fractals.2005,24(3):759–765.
[94] Tao Xiang, Kwok-wo Wong, Xiaofeng Liao. A novel symmetrical cryptosystem based ondiscretized two-dimensional chaotic map[J]. Physics Letters A,2007,364(3-4):252-258.
[95] N.K. Pareek, Vinod Patidar, K.K. Sud image encryption using chaotic logistic map[J]. Imageand Vision Computing,2006,24:926–934
[96]任勇金.基于混沌系统的图像加密算法研究[J].数字技术与应用,2012(6):134.
[97]陈果,廖晓峰.一种基于混沌映射的图像加密算法[J].计算机应用,2005,25(22):121-123.
[98]车生兵,黄达,李光.基于复合混沌的自适应图像加密算法[J].中国图像图形学报,2006,11(11):1557-1561.
[99]周志刚,徐江峰,刘嫕.一种多级变参数混沌系统的图像加密研究[J].计算机工程与应用,2010,46(23):106-110.
[100]聂文梅.一种基于Arnold和Logistic双混沌系统的数字图像加密算法[J].计算机安全,2011(5):10-13.
[101]栗风永,徐江峰.基于Hash函数和多混沌系统的图像加密算法[J].计算机工程与设计,2010,31(1）:141-144.
[102]廖晓峰,肖迪,陈勇等.混沌密码学原理及其应用[M].北京:科学出版社,2009:232-264.
[103]曹光辉.基于最小粒度全置乱的图像加密算法[J].计算机应用研究,2011:28(10):3803-3806.
[104]刘宗华.混沌动力学基础及其应用[M].北京:高等教育出版社,2006:79-96.
[105]崔笑笑.混沌时间序列处理及其应用[D].济南:山东大学,2012.
[106]韩睿,赵耿,刘山鸣等.基于混沌映射的分组密码算法[J].计算机工程,2011,37(16):120-122.
[107]邓绍江,李传东.混沌理论及其在密码学的应用[J].重庆建筑大学学报,2003(5):123-127.
[108]袁方.几种混沌密码算法的设计与研究[D].大连:大连理工大学,2011.
[109]郑凡,田小建,范文华等.基于Henon映射的数字图像加密[J].北京邮电大学学报,2008,31(1):66-70.
[110]茅耀斌,戴跃伟等.一种基于Baker映射的图像信息隐藏方法[J].南京理工大学学报自然科学版,2002,26(2):152-157.
[111]Chua L O, Komuro M, Matsumoto T. The double scroll family, part I: rigorous proof ofchaos[J]. IEEE Trans. CAS,1986,33:1072-1096.
[112]Chen G, Ueta T. Yet another chaotic attractor[J]. International Journal of Bifurcation andChaos,1999,9:1465-1466.
[113]Rossler O E. An equation for continuous chaos[J]. Physcis Letters A,1976,57:397-398.
[114]Liao X F, Wong K W, Leung C, Wu Z F. Hopf bifurcation and chaos in a single delayedneuron equation with non-monotonic activation function[J]. Chaos, Solitons&Fractals,2001,12(8):1535-1547.
[115]刘竹松,陈平华,刘怡俊.混沌数字水印技术研究进展[J].计算机应用研究,2011,28(1):1-5.
[116]柏森,胡中豫,吴乐华等.通信信息隐匿技术[M].北京:国防工业出版社,2005:94-97.
[117]廖建华.基于混沌理论的数字图像加密技术研究[D].长沙:中南大学,2010.
[118]F. Dachselt, W.Schwarz Chaos and cryptography[J]. IEEE Trans. Circuits and Syst.I,2001,48(12):1498-1509.
[119]胡向东,魏琴芳.应用密码学[M].北京:电子工业出版社,2006:39-44.
[120]段文文,李碧.基于混沌的数字图像置乱算法及参数优化[J].计算机应用研究,2011,28(1):329-331.
[121]马苗,谭永杰.基于相邻像素间位异或的图像置乱算法[J].计算机工程与应用,2008,44(9):187-189.
[122]Corron N J, Reed B R. Chaotic scrambling for wireless analog video[J]. Communications inNonlinear Science and Numerical Simulation,2010,9(15):2504-2513
[123]Li C X, Yang S Q. Chaotic scrambling and encryption method of image[J]. Journal ofQingdao University of Science and Technology,2009,1(30):80-83
[124]Yang S Q. Novel chaotic scrambling and encryption method of image[J]. ComputerEngineering,2009,10(35):135-138
[125]侯文滨,吴成茂.基于Arnold变换的图像分存加密方法[J].计算机应用.2011,31(10):2681-2686
[126]刘家胜,朱灿焰,汪一鸣,等.基于位置相关性的图像置乱效果评价方法[J].计算机工程,2010,36(24):208-210.
[127]J. Fridrich, Symmetric cipher based on two dimensional chaotic maps, International Journalof Bifurcation and Chaos,1998,8(6):1259-1284
[128]赵亮.数字混沌图像加密算法的分析与设计[D].重庆:重庆大学,2009.
[129]邹建成,李国富,齐东旭.广义Gray码及其在数字图像置乱中的应用[J].高校应用数学学报A辑,2002,17(3):363-370.
[130]Alvarez G, Montoya F, Romera M, et al. Cryptanalyzing an improved security modulatedchaotic encryption scheme using ciphertext absolute value [J]. Chaos, Solitons and Fractals,2005,23(5):1749-1756
[131]Chee C Y, Xu D L, Bishop S R. A zero-crossing approach to uncover the mask by chaoticencryption with periodic modulation [J]. Chaos, Solitons and Fractals,2004,21(5):1129-1134
[132]Yang T, Yang L B, Yang C M. Breaking chaotic secure communication using aspectrogram[J]. Physics Letters A,1998,247(1-2):105-111
[133]Yang T, Yang L B, Yang C M. Cryptanalyzing chaotic secure communication using returnmaps [J]. Physics Letters A,1998,245(6):495-510
[134]Sobhy M I, Shehata A E R. Methods of attacking chaotic encryption and countermeasures
[A].2001International Conference on Acoustics, Speech, and Signal Processing Proceedings
[C]. Canterbury: IEEE,2001:1001-1004
[135]Parker A T, Short K M. Reconstructing the keystream from a chaotic encryption scheme[J].IEEE Trans. on Circuits and Systems-I,2001,48(5):624-630
[136]Wang X G, Zhan M, Lai C H, et al. Error function attack of chaos synchronization basedencryption schemes [J]. Chaos,2004,14(1):128-137
[137]Lei M, Meng G, Feng Z J. Security analysis of chaotic communication systems based onVolterra–Wiener–Korenberg model [J]. Chaos, Solitons and Fractals,2006,28(1):264-270
[138]Wu X G, Hu H P, Zhang B L. Analyzing and improving a chaotic encryption method[J].Chaos, Solitons and Fractals2004,22(2):367-373
[139]Bateni G H, McGillem C D. A chaotic direct-sequence spread-spectrum communicationsystems [J]. IEEE Trans. Communications.1994,422(2/3/4):1524-1537
[140]Protopopescu V A, Santoro R T, Tollover J S. Fast and secure encryption–decryptionmethod based on chaotic dynamics. US Patent No.5479513,1995
[141]Li S J, Mou X Q, Cai Y L. Pseudo-random bit generator based on couple chaotic systems andits application in stream-ciphers cryptography. In Progress inCyptology-INDOCRYPT2001,Lecture Notes in Computer Science, Chennai, India:Springer-Verlag, Berlin,2001,2247:316-329
[142]Li S J, Mou X Q, Cai Y L. Improving security of a chaotic encryption approach [J].PhysicsLetters A,2001,290(3-4):127-133
[143]Hegui Zhu, Cheng Zhao, Xiangde Zhang.“A novel image encryption–compression schemeusing hyper-chaos and Chinese remainder theorem”, Image Communication, vol.28,(2013),pp.670-680.
[144]Yushu Zhang, Di Xiao, Yonglu Shu, Jing Li.“A novel image encryption scheme based on alinear hyperbolic chaotic system of partial differential equations”, Image Communication,vol.28,(2013), pp.292-300.
[145]S. Behnia, A. Akhavan, A. Akhshani, A. Samsudin.“Image encryption based on the Jacobianelliptic maps”, Journal of Systems and Software, vol.86,(2013),pp.2429-2438.
[146]Ch.-H. Lin, T.-H. Chen, Ch.-S. Wu.“A batch image encryption scheme based on chainingrandom grids”, Scientia Iranica, vol.20,(2013), pp.670-681.
[147]Atieh Bakhshandeh, Ziba Eslami.“An authenticated image encryption scheme based onchaotic maps and memory cellular automata”, Optics and Lasers in Engineering,vol.51,(2013), pp.665-673.
[148]Ahmed A. Abd El-Latif, Li Li, Ning Wang, Qi Han, Xiamu Niu.“A new approach to chaoticimage encryption based on quantum chaotic system, exploiting color spaces”, SignalProcessing, vol.93,(2013), pp.2986-3000.
[149]Gao T G, Chen Z Q, A new encryption algorithm based on hyper-chaos[J]. Phys． Lett.2008:A372394
[150]Wang J, Jiang G P. Cryptanalysis of a hyper-chaotic image encryption algorithm and itsimproved version [J]. Acta Phys. Sin,2011,60(6):060503
[151]Zhu C X, Sun K H, Cryptanalysis and improvement of a class of hyperchaos based imageencryption algorithms [J]. Acta Phys. Sin,2012,61(12):120503
[2] R Szeliski. Image Alignment and Stitching: A Tutorial[R]. Richard Szeliski Preliminarydraft,January26,2005，Technical Report，MSR-TR-2004-92.
[3]方俊伟，翟超，金熠.相位相关法实现图像拼接的仿真和优化[J].计算机应用与软件,2008,25(1):207-208
[4]王俊杰，刘家茂，胡运发等.图像拼接技术[J]．计算机科学，2003，30（6）：141-144.
[5]蔡丽欢，廖英豪，郭东辉．图像拼接方法及其关键技术研究[J]．计算机技术与发展，2008，18（3）：1-4.
[6]李弼程，彭天强，彭波.智能图像处理技术[M].北京：电子工业出版社，2004.
[7]刘卫光.图像信息融合与识别[M].北京：电子工业出版社，2008.
[8] Zhang Z., Deriche R., Faugeras O., Luong Q.T., A robust technique for matching twouncalibrated images through the recovery of the unknown epipolar geometry[J].ArtificialIntelligence Journal,1995,78:87-119
[9] Szeliski R.. Video mosaics for virtual environments[J]. IEEE Computer Graphics andApplications,1996,16(2):16-20
[10] McMillan L., Bishop G.. Plenoptic modeling: An image-based renderingsystem[C].Computer Graphics (SIGGRAPH'95),1995:39–46
[11] Zokai S., Wolberg G.. Image Registration Using Log-Polar Mappings for Recovery ofLarge-Scale Similarity and Projective Transformations[J]. IEEE Transactions on ImageProcessing,2005,14(10):1422-1434
[12] Chen S.E.. QuickTime VR-an image-based approach to virtual environment navigation.[C].Computer Graphics (SIGGRAPH'95),1995:29–38
[13]罗诗途,王艳玲,张玘，等.车载图像跟踪系统中电子稳像算法的研究[J]．光学精密工程，2005，13（1）：95-103.
[14] Alexandrov V V, Gorsky N D, Mysko S N. A fast technique for recursive scene matchingusing pyramids[J]. Pattern Recognition Letters,1985,3:413-419.
[15]张立华，徐文立.基于遗传算法的点模式匹配方法[J].电子学报，2000，28（10）：36-40.
[16] Li R X, Zeng B, Liou M L. A new three-step search algorithm for block motionestimation[J].IEEE Transactions on Circuits&Systems for Video Technology,1994，4（4）：438-442.
[17] Po L. M., Ma W. Ch, A novel four-step search algorithm for fast block motion estimation[J].IEEE Transactions on Circuits&Systems for Video Technology,1996，6（3）：313-317.
[18] Zhu S, Ma K K. A new diamond search algorithm for fast block-matching motionestimation[J]. IEEE Trans Image Processing,2000,9(2):287-290.
[19] C. Zhu, X. Lin, L. P. Chau, et al. A novel hexagon-based search algorithm for fast blockmotion estimation[C].Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, May2001.
[20] Kuglin C D, Hines D D. The phase correlation image alignment method[C]. IEEEConference on Cybernetics and Society,1975:163-175.
[21]张静，胡志萍，刘志泰，等.基于轮廓相位相关的图像自动拼接[J]．大连理工大学学报，2005，45（1）：68-74.
[22] Bailey J G, Messenr R A. Log-polar Mapping as a preprocessing stage for an image trackingsystem [J]. Intelligent Robots and Computer Vision,1988,(7):223-227.
[23] Lim F L, West G A, Venkatesh S. Use of log polar space for foveation and featurerecognition[C].IEEE:Proc-Vis. Image Signal Process,1997,144(6)
[24]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[J]．中国图像图形学报，2005，10（1）：59-63.
[25]安晓东.基于Fourier-Melllin变换的宽视场监控图像合成技术[J].中北大学学报（自然科学版），2007，28（2）：181-183.
[26]李晓明，赵训坡，郑链，等.基于Fourier-Melllin变换的图像配准方法及应用拓展[J].计算机学报，2006，29（3）：466-472.
[27] Chen Q S, Defrise M, Deconinck F.Symmetric Phase-only matched filtering ofFourier-Mellin transforms for image registration and recognition[J].IEEE Transactions onPattern Analysis and Machine Intelligence,1994，16（12）：1156-1168.
[28] Ji Zhou, Jiaoying shi. A robust algorithm for feature point matching[J].Computers&Graphics,2002,26:429-436.
[29] Dai X L, Khorram S. A feature-based image registration algorithm using improvedchain-code representation combined with invariant moments[J]. IEEE Trans. on Geoscienceand Remote Sensing,1999,37(5):2351-2362.
[30] Mallat S. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation[J].IEEE Trans. on Pattern Anal. Mach. Intell.,1989,11(7):674-693.
[31] Tian G Y, Gledhill Duke， Taylor D. Comprehensive interest points based imagingmosaic.Pattern Recognition Letters,2003,24:1171–1179.
[32] Gao G D, Jia K B. A New Image Mosaics Algorithm Based on Feature PointsMatching[C].IEEE,2007,0-7695-2882-1/07.
[33] Yang ZL, Guo B L. Image mosaic based on SIFT[C]. International Conference on IntelligentInformation Hiding and Multimedia Signal Processing,2008,1422-1425.
[34] M Brown, D G Lowe. Recognising Panoramas. Proceedings of IEEE InternationalConference on Computer Vision,2003,1218-1225.
[35]王君本，卢选民，贺兆.一种基于快速鲁棒特性的图像匹配算法[J].计算机科学与工程，2011,33(2):112-117.
[36]刘恩鹏.仪表图像识别读表技术研究与系统实现[D].徐州：中国矿业大学，2012.
[37]刘卫光.图像信息融合与识别[M].北京：电子工业出版社，2008.
[38]孙兆林. MATLAB6.x图像处理[M].北京：清华大学出版社，2002.
[39] L. Brown. A survey of image registration techniques[J]. ACM Computer Surveys,1992,24(4):325-376.
[40]李国胜，林宗坚，任超锋等.相机校验中标志点的自动提取[J].测绘科学，2010,35(6):161-163.
[41]曹俊.基于虚拟点的可见光和SAR图像配准研究[J].光电工程，2009,36(11):79-84.
[42]王静.基于SIFT和角点检测的自动图像配准方法研究[D].武汉：华中科技大学，2010.
[43]周鹏，谭勇，徐守时.基于角点检测图像配准的一种新算法[J].中国科学技术大学学报，2002,32(4):455-461.
[44]那彦，焦李成.基于多分辨率分析理论的图像融合方法[M].西安：西安电子科技大学出版社，2007.
[45]刘卫光.图像信息融合与识别[M].北京：电子工业出版社，2008.
[46] J. Canny. A computational approach to edge detection[J]. IEEE Transactions on PatternAnalysis and Machine Intelligence,1986,8(6):679-698.
[47]李实英，杨高波.特征提取与图像处理（第二版）[M].北京：电子工业出版社，2011.
[48]李俊山，杨威，张雄美.红外图像处理、分析与融合[M].北京：科学出版社，2009.
[49] S. M. Smith and J. M. Brady. SUSAN-a new approach to low level image processing[J].International Journal of Computer Vision,1997,23(1):45-78.
[50]夏良正.数字图像处理编程入门[M].江苏：东南大学出版社，1999.
[51]阮秋琦，阮志宇.数字图像处理（冈萨雷斯）第二版[M].北京：电子工业出版社，2008.
[52] D. Lowe. Object Recognition from Local Scale-Invariant Features[C]. InternationalConference on Computer Vision,1999,1150-1157.
[53] D. Lowe. Distictive Image Features from Scale-Invariant Keypoints[J]. International Journalof Computer Vision,2004,60(2):91-110
[54]王山虎,尤红建,付琨.基于大尺度双边SIFT的SAR图像同名点自动提取方法[J].电子与信息学报,2012,34(2):287-293.
[55]陈抒瑢,李勃,董蓉,陈启美. Contourlet-SIFT特征匹配算法[J].电子与信息学报,2012,35(5):1215-1221.
[56]唐朝伟,肖健,邵艳清,苗光胜.一种改进的SIFT描述子及其性能分析[J].武汉大学学报,2012,37(1):11-16.
[57]胡珂立,谷宇章,王营冠,邹方圆,金锋.基于分水岭分割和尺度不变特征点的多目标全自主跟踪算法[J].电子与信息学报,2012,34(8):1827-1832.
[58] Huo Ju, Yang Ning, Cao Mao-yong, and Yang Ming. A reliable algorithm for image matchingbased on SIFT[J]. Jounal of Harbin Institute of Technology,2012,19(4):90-95.
[59] Mikolajczyk K and Schmid C. A performance evaluation of local descriptors[J]. IEEETransactions on Pattern Analysis an Machine Intelligences,2005,27(10):1615-1630.
[60] Mikolajczyk K, Matas J. Improving descriptors for fast tree matching by optimal linearprojection[C]. In: Proceedings of the11th IEEE International Conference on ComputerVision. Rio de Janeiro, Brazil: IEEE,2007,1-8
[61] Klare B, Li Z F, Jain A K. Matching forensic sketches to mug shot photos[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2011,33(3):639-646
[62] Yan K, Sukthankar R. PCA-SIFT: A More Distinctive Representation for Local ImageDescriptors[C]. In: Proceedings of the Conference on Computer Vision and PatternRecognition. Washington, USA:IEEE,2004,1,511-517.
[63] Bay H, Tuytelaars T, and Gool Van J L. SURF: Speeded Up Robust Features[C]. EuropeanConference on Computer Vision, Graz, Austria, May,2006:404-417.
[64]曾峦,顾大龙.一种基于扇形区域分割的SIFT特征描述符[J].自动化学报,2012,38(9):1513-1519.
[65]张良,王海丽,吴仁彪.基于改进局部不变特征的兴趣点匹配[J].电子与信息学报.2009,31(11):2620-2625.
[66] Kaiyang Liao, Guizhong Liu, and Youshi Hui. An improvement to the SIFT descriptor forimage representation and matching[J]. Pattern Recognition Letters,2013(34):1211-1220.
[67] C. Harris and M. Stephens. A Combined Corner and Edge Detector[C]. In The4thAlveyVision Conference Manchester UK:147-151.
[68]王民钢，王超，樊英平.基于Harris角点检测改进算法[J].计算机仿真，2012,11:294-299.
[69] S. M. Smith, J. M. Brandy. SUSAN-A New Approach to Low Level Image Processing[J].International Journal of Computer Vision,1997,23(1):45-87.
[70]周乐.基于SUSAN检测算子的二维条码定位方法[J].微型机与应用，2012,31(9):35-38.
[71] T, Lindeberg. Scale-space theory: A basic tool for analyzing structures at different scales.Journal of Applied Statistics，1994，21(2):224-270.
[72]蔡勇，刘学慧，吴恩华.基于图像绘制的虚拟显示系统环境[J].软件学报，1997,8(10):721-728.
[73]杨萍.基于图像拼接的车载全视角观测器研究与实现[D].北京：北京工业大学，2011.
[74] K. Mikolajczyk. Detection of local features invariant to affine transformations:[ph.D.thesis].Institut National Polytechnique de Grenoble, France,2002.
[75]王永明，王贵锦.图像局部不变性特征与描述[M].北京：国防工业出版社，2010.
[76] Schmid.C, Mohr.R, Bauckhage.C. Eva1uation of Interest point Detectors[J]. Int. Joumal ofComPuter Vision,2000,37(2):151-172.
[77]李欢欢，黄山，张洪斌.基于Harris和SIFT算法的自动图像拼接[J].计算机工程与科学，2012,11:104-108.
[78]党建武，宗岩，王阳萍.基于SIFT特征检测的图像拼接优化算法研究[J].计算机应用研究，2012,29(1):329-333.
[79] Johnson A, Hebert M. Using spin images for efficient object recognition in cluttered3dscenes[J]. IEEE Trans. PAMI,1999,21(5).
[80]朱志文,沈占峰,骆剑承.改进SIFT点特征的并行遥感影像配准[J].遥感学报,2011,15(5):1032-1039.
[81] Friedman.J.H, Bentley J.L, Finkel.R.A..Analgorithm for finding best matches in logarithmicexpected time[J].ACM Transactions on Mathematical Software,1977,3(3):209-226.
[82] Jeffrey S. Beis, D. G. Lowe. Shape indexing using approximate nearest neighbor search inhigh-dimensional spaces[C]. In Conference on Computer Vision and Pattern Recognition,Puerto Rico,1997,1000-1006.
[83] M.A. Fischler, R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting withApplications to Image Analysis and Automated Cartography[J]. Comm. Of theACM,1981:381-395.
[84]周乐.基于SUSAN检测算子的二维条码定位方法[J].微型机与应用，2012,31(9):35-38.
[85] D. G. Feng, X. Y. Wang. Progress and Prospect of some Fundamental Research onInformation Security in China[J]. J. Comput. Sci. Technol.,2006,21(5):740–755.
[86] P. Moulin, R. Koetter. Data Hiding Codes[J]. P. IEEE,2008,12:2083–2126.
[87] C. E. Shannon. Communication theory of secrecy systems[J]. Bell Systems Technical Journal,1949,28(4):656-715.
[88] Matthews R. On the derivation of a “chaotic” encryption algorithm[J]. Cryptologia,1989,13:29-36.
[89] Marco Tomassini, Malthieu Perrenoud. Cryptography with cellular automata[J]. Applied SoftComputing,2001,1(2):151-160.
[90] Kennedy J, Eberhart R. Particle swarm optimization[C]. IEEE International Conference onNeural Networks, Perth, Australia,1995,4:1942-1948.
[91] Guo Chen, Yong Chen, Xiaofeng Liao. An extended method for obtaining S-boxes based onthree-dimensional chaotic Baker maps[J]. Chaos, Solitons and Fractals,2007,31:571–579.
[92] Di Xiao, Xiaofeng Liao, K..W. Wong. An efficient entire chaos-based scheme for deniableau-thentication[J]. Chaos, Solitons and Fractals,2005,23(4):1327–1331.
[93] Linhua Zhang, Xiaofeng Liao, Xuebing Wang. An image encryption approach based onchaotic maps[J]. Chaos, Solitons&Fractals.2005,24(3):759–765.
[94] Tao Xiang, Kwok-wo Wong, Xiaofeng Liao. A novel symmetrical cryptosystem based ondiscretized two-dimensional chaotic map[J]. Physics Letters A,2007,364(3-4):252-258.
[95] N.K. Pareek, Vinod Patidar, K.K. Sud image encryption using chaotic logistic map[J]. Imageand Vision Computing,2006,24:926–934
[96]任勇金.基于混沌系统的图像加密算法研究[J].数字技术与应用,2012(6):134.
[97]陈果,廖晓峰.一种基于混沌映射的图像加密算法[J].计算机应用,2005,25(22):121-123.
[98]车生兵,黄达,李光.基于复合混沌的自适应图像加密算法[J].中国图像图形学报,2006,11(11):1557-1561.
[99]周志刚,徐江峰,刘嫕.一种多级变参数混沌系统的图像加密研究[J].计算机工程与应用,2010,46(23):106-110.
[100]聂文梅.一种基于Arnold和Logistic双混沌系统的数字图像加密算法[J].计算机安全,2011(5):10-13.
[101]栗风永,徐江峰.基于Hash函数和多混沌系统的图像加密算法[J].计算机工程与设计,2010,31(1）:141-144.
[102]廖晓峰,肖迪,陈勇等.混沌密码学原理及其应用[M].北京:科学出版社,2009:232-264.
[103]曹光辉.基于最小粒度全置乱的图像加密算法[J].计算机应用研究,2011:28(10):3803-3806.
[104]刘宗华.混沌动力学基础及其应用[M].北京:高等教育出版社,2006:79-96.
[105]崔笑笑.混沌时间序列处理及其应用[D].济南:山东大学,2012.
[106]韩睿,赵耿,刘山鸣等.基于混沌映射的分组密码算法[J].计算机工程,2011,37(16):120-122.
[107]邓绍江,李传东.混沌理论及其在密码学的应用[J].重庆建筑大学学报,2003(5):123-127.
[108]袁方.几种混沌密码算法的设计与研究[D].大连:大连理工大学,2011.
[109]郑凡,田小建,范文华等.基于Henon映射的数字图像加密[J].北京邮电大学学报,2008,31(1):66-70.
[110]茅耀斌,戴跃伟等.一种基于Baker映射的图像信息隐藏方法[J].南京理工大学学报自然科学版,2002,26(2):152-157.
[111]Chua L O, Komuro M, Matsumoto T. The double scroll family, part I: rigorous proof ofchaos[J]. IEEE Trans. CAS,1986,33:1072-1096.
[112]Chen G, Ueta T. Yet another chaotic attractor[J]. International Journal of Bifurcation andChaos,1999,9:1465-1466.
[113]Rossler O E. An equation for continuous chaos[J]. Physcis Letters A,1976,57:397-398.
[114]Liao X F, Wong K W, Leung C, Wu Z F. Hopf bifurcation and chaos in a single delayedneuron equation with non-monotonic activation function[J]. Chaos, Solitons&Fractals,2001,12(8):1535-1547.
[115]刘竹松,陈平华,刘怡俊.混沌数字水印技术研究进展[J].计算机应用研究,2011,28(1):1-5.
[116]柏森,胡中豫,吴乐华等.通信信息隐匿技术[M].北京:国防工业出版社,2005:94-97.
[117]廖建华.基于混沌理论的数字图像加密技术研究[D].长沙:中南大学,2010.
[118]F. Dachselt, W.Schwarz Chaos and cryptography[J]. IEEE Trans. Circuits and Syst.I,2001,48(12):1498-1509.
[119]胡向东,魏琴芳.应用密码学[M].北京:电子工业出版社,2006:39-44.
[120]段文文,李碧.基于混沌的数字图像置乱算法及参数优化[J].计算机应用研究,2011,28(1):329-331.
[121]马苗,谭永杰.基于相邻像素间位异或的图像置乱算法[J].计算机工程与应用,2008,44(9):187-189.
[122]Corron N J, Reed B R. Chaotic scrambling for wireless analog video[J]. Communications inNonlinear Science and Numerical Simulation,2010,9(15):2504-2513
[123]Li C X, Yang S Q. Chaotic scrambling and encryption method of image[J]. Journal ofQingdao University of Science and Technology,2009,1(30):80-83
[124]Yang S Q. Novel chaotic scrambling and encryption method of image[J]. ComputerEngineering,2009,10(35):135-138
[125]侯文滨,吴成茂.基于Arnold变换的图像分存加密方法[J].计算机应用.2011,31(10):2681-2686
[126]刘家胜,朱灿焰,汪一鸣,等.基于位置相关性的图像置乱效果评价方法[J].计算机工程,2010,36(24):208-210.
[127]J. Fridrich, Symmetric cipher based on two dimensional chaotic maps, International Journalof Bifurcation and Chaos,1998,8(6):1259-1284
[128]赵亮.数字混沌图像加密算法的分析与设计[D].重庆:重庆大学,2009.
[129]邹建成,李国富,齐东旭.广义Gray码及其在数字图像置乱中的应用[J].高校应用数学学报A辑,2002,17(3):363-370.
[130]Alvarez G, Montoya F, Romera M, et al. Cryptanalyzing an improved security modulatedchaotic encryption scheme using ciphertext absolute value [J]. Chaos, Solitons and Fractals,2005,23(5):1749-1756
[131]Chee C Y, Xu D L, Bishop S R. A zero-crossing approach to uncover the mask by chaoticencryption with periodic modulation [J]. Chaos, Solitons and Fractals,2004,21(5):1129-1134
[132]Yang T, Yang L B, Yang C M. Breaking chaotic secure communication using aspectrogram[J]. Physics Letters A,1998,247(1-2):105-111
[133]Yang T, Yang L B, Yang C M. Cryptanalyzing chaotic secure communication using returnmaps [J]. Physics Letters A,1998,245(6):495-510
[134]Sobhy M I, Shehata A E R. Methods of attacking chaotic encryption and countermeasures
[A].2001International Conference on Acoustics, Speech, and Signal Processing Proceedings
[C]. Canterbury: IEEE,2001:1001-1004
[135]Parker A T, Short K M. Reconstructing the keystream from a chaotic encryption scheme[J].IEEE Trans. on Circuits and Systems-I,2001,48(5):624-630
[136]Wang X G, Zhan M, Lai C H, et al. Error function attack of chaos synchronization basedencryption schemes [J]. Chaos,2004,14(1):128-137
[137]Lei M, Meng G, Feng Z J. Security analysis of chaotic communication systems based onVolterra–Wiener–Korenberg model [J]. Chaos, Solitons and Fractals,2006,28(1):264-270
[138]Wu X G, Hu H P, Zhang B L. Analyzing and improving a chaotic encryption method[J].Chaos, Solitons and Fractals2004,22(2):367-373
[139]Bateni G H, McGillem C D. A chaotic direct-sequence spread-spectrum communicationsystems [J]. IEEE Trans. Communications.1994,422(2/3/4):1524-1537
[140]Protopopescu V A, Santoro R T, Tollover J S. Fast and secure encryption–decryptionmethod based on chaotic dynamics. US Patent No.5479513,1995
[141]Li S J, Mou X Q, Cai Y L. Pseudo-random bit generator based on couple chaotic systems andits application in stream-ciphers cryptography. In Progress inCyptology-INDOCRYPT2001,Lecture Notes in Computer Science, Chennai, India:Springer-Verlag, Berlin,2001,2247:316-329
[142]Li S J, Mou X Q, Cai Y L. Improving security of a chaotic encryption approach [J].PhysicsLetters A,2001,290(3-4):127-133
[143]Hegui Zhu, Cheng Zhao, Xiangde Zhang.“A novel image encryption–compression schemeusing hyper-chaos and Chinese remainder theorem”, Image Communication, vol.28,(2013),pp.670-680.
[144]Yushu Zhang, Di Xiao, Yonglu Shu, Jing Li.“A novel image encryption scheme based on alinear hyperbolic chaotic system of partial differential equations”, Image Communication,vol.28,(2013), pp.292-300.
[145]S. Behnia, A. Akhavan, A. Akhshani, A. Samsudin.“Image encryption based on the Jacobianelliptic maps”, Journal of Systems and Software, vol.86,(2013),pp.2429-2438.
[146]Ch.-H. Lin, T.-H. Chen, Ch.-S. Wu.“A batch image encryption scheme based on chainingrandom grids”, Scientia Iranica, vol.20,(2013), pp.670-681.
[147]Atieh Bakhshandeh, Ziba Eslami.“An authenticated image encryption scheme based onchaotic maps and memory cellular automata”, Optics and Lasers in Engineering,vol.51,(2013), pp.665-673.
[148]Ahmed A. Abd El-Latif, Li Li, Ning Wang, Qi Han, Xiamu Niu.“A new approach to chaoticimage encryption based on quantum chaotic system, exploiting color spaces”, SignalProcessing, vol.93,(2013), pp.2986-3000.
[149]Gao T G, Chen Z Q, A new encryption algorithm based on hyper-chaos[J]. Phys． Lett.2008:A372394
[150]Wang J, Jiang G P. Cryptanalysis of a hyper-chaotic image encryption algorithm and itsimproved version [J]. Acta Phys. Sin,2011,60(6):060503
[151]Zhu C X, Sun K H, Cryptanalysis and improvement of a class of hyperchaos based imageencryption algorithms [J]. Acta Phys. Sin,2012,61(12):120503