MIDI音频隐写和隐写分析
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摘要
隐写和隐写分析是信息隐藏技术两个主要领域,隐写(Steganography)是将秘密信息隐藏到载体中,隐写分析(Steganalysis)是对隐写的攻击,目的是检测隐秘通信中的秘密信息,破坏秘密通信。近年来,随着信息隐藏技术的发展,出现了以音乐设备数字接口(MIDI)音频为载体的隐写和隐写分析方法。MIDI是音乐设备和计算机之间传输音乐信息的协议标准,MIDI文件存储的是如何播放音乐的指令,具有容量小,便于编辑等优点,在手机及互联网领域有广泛应用。所以针对MIDI音频的信息隐藏技术研究具有重要意义。
由于MIDI音频存储的是MIDI指令,MIDI信息隐藏技术与图像载体及其他音频载体隐藏既有相似之处又有不同特点。所以本文在国家自然科学基金的资助下,针对MIDI载体的信息隐藏技术进行研究,论文的主要工作和创新点如下:
1分析音频隐写的机理,总结音频隐写和隐写分析方法,提出了较为完善音频隐写和隐写分析评价模型,总结目前MIDI音频的隐写方法和隐写分析方法,按照评价模型比较各方法的优劣。
2目前只有针对MIDI音频LSB替换的隐写分析方法,本文针对MIDI音频LSB匹配隐写,提出基于力度平滑度转换率的隐写分析算法,利用匹配隐写改变MIDI力度平滑度,训练平滑度转换率阈值,判断MIDI音频是否存在隐写;并根据匹配隐写的加性噪声特性,给出嵌入率的一种估计方法。实验表明,在嵌入率大于20%时,检测正确率可以达到70%以上,对嵌入率的估计也比较准确,估计偏差在10%之内。
3为了提高MIDI音频LSB隐写分析检测效率,提出基于特征提取和模式识别的隐写分析算法。提取直方图特征函数(HCF)域21维统计矩特征组成特征向量,用支持向量机(SVM)训练分类器。实验表明,分别对LSB替换、匹配隐写进行分析,平均分类正确率可以达到90%以上。并利用MIDI音频分通道存储的特性,给出了特征提取的优化方法,经过实验分析,优化分析算法的分类正确率和适用性均有提高。
4针对MIDI音频LSB、省略指令和同步指令三种隐写方法,分析隐写后MIDI文件结构特征和统计特征,提出一种MIDI隐写分析的盲分类器的设计方法。实验表明,盲分类器可以实现MIDI音频三种隐写方法的有效检测。根据MIDI音频LSB隐写和隐写分析方法,实现了一个简单在线MIDI音频隐写和分析系统,系统采用浏览器服务器(B/S)模式,能够实现远程在线对MIDI音频的LSB隐写和隐写分析。
Steganography and steganalysis are two main areas of Information hiding technology. Steganography is to hide secret information into the carrier. Steganalysis purposed is to detect and destroy the secret information is an attack on steganography. In recent years, with the development of information hiding technology, MIDI audio is being the carrier hidden secret message. MIDI file with the advantage of small size and editing easy is stored on the instructions of how to play music. MIDI is a music information transmission protocol standard between Music equipment and computers, which is widely used in the field of mobile phones and the internet. As a result, MIDI audio information hiding technique is more important.
MIDI audio is stored as MIDI commands. There are both similarities and differences between MIDI file information hiding and other carieers. This paper mainly research on MIDI audio information hiding supported by National Natural Science Foundation, thesis work and innovations are as follows:
1 Summary audio steganography and steganalysis method, proposed a more complete audio steganography and steganalysis evaluation model. Summarize the MIDI audio steganographic methods and steganalysis methods according to the evaluation model.
2 As there is only MIDI audio LSB replacement Steganalysis method, a method for MIDI audio LSB matching is proposed in the paper. The method judge a MIDI audio file by training smoothness conversion rate threshold, as the process of Steganography would change velocity smoothness of the MIDI file. The method can return a probable embedding rate based on additive noise feature of the steganography. Experiments show that when the embedding rate greater than 20%, the correct detection rate can reach more than 70%, also the error of the embedding rate is less than 10%.
3 In order to improve MIDI audio LSB detection efficiency, a steganalysis algorithm is proposed based on the characteristic mining and pattern recognition. Extract 21-dimensional statistical characteristics of histogram characteristic function (HCF) field, training classifier with the support vector machine (SVM). Experiments show that the average classification accuracy rate can be above 90% for either LSB replacement or matching steganography. Finally, propose a LSB steganalysis optimization feature extraction methods using the characteristic of MIDI audio sub-channel storage. Experiments show that both classification accuracy and applicability increased.
4 A MIDI blind steganalysis classifier was designed by Analyzing MIDI files structural features and statistical features for MIDI audio LSB steganography, omitted instructions steganography and synchronization instructions steganography. Experiments show that the blind classifier can detect three methods steganography of MIDI audio effectively. Finally, design a simple online steganography and steganalysis system using the browser and server (B/S) mode, which can achieve LSB steganography and Steganalysis of MIDI audio online.
由于MIDI音频存储的是MIDI指令,MIDI信息隐藏技术与图像载体及其他音频载体隐藏既有相似之处又有不同特点。所以本文在国家自然科学基金的资助下,针对MIDI载体的信息隐藏技术进行研究,论文的主要工作和创新点如下:
1分析音频隐写的机理,总结音频隐写和隐写分析方法,提出了较为完善音频隐写和隐写分析评价模型,总结目前MIDI音频的隐写方法和隐写分析方法,按照评价模型比较各方法的优劣。
2目前只有针对MIDI音频LSB替换的隐写分析方法,本文针对MIDI音频LSB匹配隐写,提出基于力度平滑度转换率的隐写分析算法,利用匹配隐写改变MIDI力度平滑度,训练平滑度转换率阈值,判断MIDI音频是否存在隐写;并根据匹配隐写的加性噪声特性,给出嵌入率的一种估计方法。实验表明,在嵌入率大于20%时,检测正确率可以达到70%以上,对嵌入率的估计也比较准确,估计偏差在10%之内。
3为了提高MIDI音频LSB隐写分析检测效率,提出基于特征提取和模式识别的隐写分析算法。提取直方图特征函数(HCF)域21维统计矩特征组成特征向量,用支持向量机(SVM)训练分类器。实验表明,分别对LSB替换、匹配隐写进行分析,平均分类正确率可以达到90%以上。并利用MIDI音频分通道存储的特性,给出了特征提取的优化方法,经过实验分析,优化分析算法的分类正确率和适用性均有提高。
4针对MIDI音频LSB、省略指令和同步指令三种隐写方法,分析隐写后MIDI文件结构特征和统计特征,提出一种MIDI隐写分析的盲分类器的设计方法。实验表明,盲分类器可以实现MIDI音频三种隐写方法的有效检测。根据MIDI音频LSB隐写和隐写分析方法,实现了一个简单在线MIDI音频隐写和分析系统,系统采用浏览器服务器(B/S)模式,能够实现远程在线对MIDI音频的LSB隐写和隐写分析。
Steganography and steganalysis are two main areas of Information hiding technology. Steganography is to hide secret information into the carrier. Steganalysis purposed is to detect and destroy the secret information is an attack on steganography. In recent years, with the development of information hiding technology, MIDI audio is being the carrier hidden secret message. MIDI file with the advantage of small size and editing easy is stored on the instructions of how to play music. MIDI is a music information transmission protocol standard between Music equipment and computers, which is widely used in the field of mobile phones and the internet. As a result, MIDI audio information hiding technique is more important.
MIDI audio is stored as MIDI commands. There are both similarities and differences between MIDI file information hiding and other carieers. This paper mainly research on MIDI audio information hiding supported by National Natural Science Foundation, thesis work and innovations are as follows:
1 Summary audio steganography and steganalysis method, proposed a more complete audio steganography and steganalysis evaluation model. Summarize the MIDI audio steganographic methods and steganalysis methods according to the evaluation model.
2 As there is only MIDI audio LSB replacement Steganalysis method, a method for MIDI audio LSB matching is proposed in the paper. The method judge a MIDI audio file by training smoothness conversion rate threshold, as the process of Steganography would change velocity smoothness of the MIDI file. The method can return a probable embedding rate based on additive noise feature of the steganography. Experiments show that when the embedding rate greater than 20%, the correct detection rate can reach more than 70%, also the error of the embedding rate is less than 10%.
3 In order to improve MIDI audio LSB detection efficiency, a steganalysis algorithm is proposed based on the characteristic mining and pattern recognition. Extract 21-dimensional statistical characteristics of histogram characteristic function (HCF) field, training classifier with the support vector machine (SVM). Experiments show that the average classification accuracy rate can be above 90% for either LSB replacement or matching steganography. Finally, propose a LSB steganalysis optimization feature extraction methods using the characteristic of MIDI audio sub-channel storage. Experiments show that both classification accuracy and applicability increased.
4 A MIDI blind steganalysis classifier was designed by Analyzing MIDI files structural features and statistical features for MIDI audio LSB steganography, omitted instructions steganography and synchronization instructions steganography. Experiments show that the blind classifier can detect three methods steganography of MIDI audio effectively. Finally, design a simple online steganography and steganalysis system using the browser and server (B/S) mode, which can achieve LSB steganography and Steganalysis of MIDI audio online.
引文
[1]吕锐,音频信息隐藏算法的研究[D].信息工程大学硕士毕业论文.郑州,2007.4.
[2] KatzenbeiSSer S,petiteola F.信息隐藏技术——隐写术与数字水印[M].吴秋新等译.北京:人民邮电出版社,2001年.
[3]杜江.信息隐藏与数字水印技术研究[D].西安:西安电子科技大学,2001.
[4]孔祥维.信息安全中的信息隐藏理论和方法研究[D].大连:大连理工大学,2003.
[5]张立和等.透视信息隐藏[M].北京:国防工业出版社,2007年.
[6]诸挥明,梁路光,付妍.人耳听阈曲线的测定[J].大学物理实验, 2006, 16(1): 43-46.
[7] BAUER B B , TORICK E L. Researches in loudness measurement [J]. IEEE Transactions on Audio and Electro acoustics, 1966, 3(14):141-151.
[8]袁开国,杨榆,杨义先.音频信息隐藏技术研究[J].中兴通讯技术,2007,13(5):06-09.
[9]谢志文,尹俊勋,饶丹.空间掩蔽效应的实验研究[J].声学学报,2006,31(4):363-369.
[10]曹汉强,张歆昱,向华.一种基于掩蔽效应的数字音频隐藏方案[J],2006,2:42-44.
[11]同鸣,姬红兵,刘晓军.基于相位编码的音频信息隐藏方法[J],2008,34(9):7-9.
[12] Tong Ming, Bian Zhengzhong. Study on Phase Perception in Speech[J]. Journal of Electronics[J], 2003, 20(5): 387-392.
[13]刘子菡,陈小平.人耳听觉对声音相位感知能力的研究[J].世界专业音响和灯光. 2009,10:6-11.
[14]杨正琴,音频隐写分析技术的研究[D],南京:南京理工大学硕士论文,2006年.
[15]吴明巧,数字图像信息隐写与隐写分析技术研究[D],长沙:国防科技大学博士论文,2007年.
[16]刘秀娟,郭立,邱天.改进的大容量多分辨率LSB音频隐写算法[J].计算机工程与应用. 2006, 30:23-25.
[17] Chen N, Zhu J. Blind multiple watermarking algorithm for audio [J]. Journal of Southeast University (English Edition). 2007, 23(2):207-210.
[18]赵朝阳,刘振华,王挺.数字音频信号的回声数据隐藏技术[J].计算机应用研究. 2000,7:42-44.
[19]黄昊,胡涛.一种新的自适应扩频数字音频水印[J]. 2007, 26(4):723-727.
[20] Kirovski D, Malvar H S. Spread-Spectrum Watermarking of Audio Signals [J]. IEEE Transactions on Signal Processing,2003, 51( 4) : 1020-1033.
[21]陈铭,张汝,刘凡凡等.基于DCT域QIM的音频信息伪装算法[J].通信学报. 2009, 30(8):105-111.
[22]马冀平,韩纪庆. DCT域音频水印嵌人对策和算法[J].电子学报,2006, 34(7): 1260-1264.
[23]高海英.基于Huffman编码的MP3隐写算法[J].中山大学学报(自然科学版),2007,46(4):32-35.
[24]陈庆元,刘振华,吕述望.基于载体结构特征的隐写分析[J].计算机工程与应用. 2004, 40(34):39-41.
[25] Pedram H, Vidyasagar P, and Elizabeth C. A Survey of Steganographic and Steganalytic Tools for the Digital Forensic Investigator [EB/OL]. http://debii.curtin.edu.au/~pedram/images/docs/survey_of_steganography_and_steganalytic_tools.pdf. July 2007.
[26]葛秀慧,田浩,郭立甫等,信息隐藏原理及应用[M],北京:清华大学出版社,2008.10.
[27] Petitcolas FAP, Anderson RJ, Kuhn MG. Information hiding-A survey[C]. Proceedings of the IEEE, 1999,87(7):1062~1078.
[28]杨帆. MIDI音频隐写分析研究[D].合肥:中国科学技术大学硕士论文,2009年.
[29] Westfeld A, Ptzmann A. Attacks on Steganographic Systems[C]. Proc.of the 3rd International Workshop on Information Hiding. Dresden, Germany,1999.
[30] Fridrich J,Goljan M,Du R.Reliable Detection of LSB Steganography in Grayscale and Color Images[C]. Proc.of ACM, Special Session on Multimedia Security and Watermarking. Ottawa, Canada,2001.
[31] Dumitrescu S, Wu Xiaolin, Memon N. On Steganalysis of Random LSB Embedding incontinous-tone Images[C]. Proc.of IEEE ICIP’02.New York,USA,2002.
[32] Dumitrescu S,Wu Xiaolin,Wang Zhe.Detection of LSB Steganography via Sample Pair Analysis[C]. Proc.of the 5th Inter-national Workshop on Information Hiding. Springer-Verlag, 2002.
[33] Zhang Tao,Ping Xijian. Reliable Detection of LSB Steganography Based on the Difference Image Histogram[C]. Proc.of IEEE Inter-national Conference on Acoustics,Speech,and Signal Processing.Hong Kong,China,2003.
[34] Zhang XP, Wang SZ, Zhang KW. Steganography with Least Histogram Abnormality[J]. Computer Network Security, Lecture Notes in Computer Science, Springer-Verlag, 2003,2776(2): 395-406
[35] Li Xiaolong, Yang Bin, Cheng Daofang, et al. A Generalization of LSB Matching [J]. IEEE Signal Processing Letters, 2009(16):69-72.
[36] Andrew D.Ker. Steganalysis of LSB Matching in Grayscale Images [J]. IEEE Signal Processing Letters,2005(12):441-444.
[37] Zhang Jun, Cox Ingemar, Doerr Gwenael. Steganalysis for LSB Matching in Images withHigh-frequency Noise [J]. Proceedings of the IEEE Workshop on Multimedia Signal Processing, 2007(10):385-388.
[38] Fridrich J.Soukal D.Goljan M.Maximum likelihood estimation of length of secret message embedded using士K steganography in spatial domain. SPIE 568 1 : Security , Steganography,and Watermarking of Multimedia Contents. San Jose:SPIE Press,2005:595-606
[39]田源,程义民,钱振兴等.基于PCA及SVM的图像信息隐藏检测[J].中国科学院研究生院学报. 2008,25(1):74-79.
[40]杨榆,雷敏,钮心祈等.基于回声隐藏的VDSC隐写分析算法[J].通信学报. 2009, 30(2):83-88.
[41]汪云路,程义民,谢春辉等.基于统计特征的语言回声隐藏分析[J].数据采集与处理.2009,24(4):408-412.
[42]黄昊,郭立,李琳.基于失真测度的直接扩频音频隐写分析[J].中国科学院研究生院学报. 2008,25(2):251-256.
[43]马懿,张政宝,冯帆等.基于小波奇异性的扩频隐写检测算法[J].计算机工程. 2009, 35(15):159-161.
[44]张飞,平西建,张涛.基于DCT系数值对差异的JSteg隐写嵌入率估计法[J].数据采集与处理. 2009,24(2):154-158.
[45]王舰,汤光明.基于冗余零系数消除的F5改进算法[J].计算机工程. 2009, 35(16):164-166.
[46] J. Fridrich. Feature-based Steganalysis for JPEG images and its implications for future design of stegano-graphic schemes[C]. Information Hiding, 6th International Workshop, volume 3200 of Lecture Notes in Computer Science, pages 67–81, 2005.
[47] Mengyu Qiao Sung, A.H. Qingzhong Liu. Feature Mining and Intelligent Computing for MP3 Steganalysis. Bioinformatics[C], Systems Biology and Intelligent Computing, 2009. IJCBS '09.
[48] Fung G,Mangasarian OL. Increment support vector machine classification[R]. Wisconsin: University of Wisconsin ,2001.
[49] McCallum A, Nigam K. A Comparison of Event Models for Naive Bayes Text Classification[J]. Learning for Text Categorization: Papers from the AAAI Workshop. WS- 98- 05, 1998:41- 48.
[50] Nigam K, Lafferty J, McCallum A. Using maximum entropy for text classification [J]. In: IJCAI99 Workshop on Machine Learning for Information Filtering. 1999: 61- 67.
[51] Yang Y, Chut C. An Example-Based Mapping Method for Text Classification and Retrieval [J]. ACMTransactions on Information Systems, 1994, 23(3): 252- 277.
[52] Vapnik V N. The Nature of Statistical Learning Theory[M], NY: Springer?Verlag, 1995张学工译.统计学习理论的本质.北京:清华大学出版社,1999.
[53]邓诗智,刘九芬,张卫明.隐写分析算法的量化评估方案[J].东南大学学报(自然科学版). 2007,37(Ⅰ):76-80.
[54] Alexander Adli, Zensho Nakao. Three Steganography Algorithms for MIDI Files [C]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics. Guangzhou, 2005:18-21.
[55] Matthew W, Beaverton. MIDI File Steganography[P]. United States Patent. No:US7402744B1, Jul.22,2008.
[56] Inoue D, Tsutomu M. A Scheme of Standard MIDI Files Steganography and Its Evaluation[J]. Security and Watermarking of Multimedia Contents IV,2002,4675:194-205
[57] Kotaro Y, Munetoshi I. A Standard MIDI File Steganography Based on Fluctuation of Duration [J]. 2009 International Conference on Availability, Reliability and Security.774-779.
[58] INOUE D, SUZUKI M, MATSUMOTO T. Detection-Resistant Steganography for Standard MIDI Files[J]. IEICE TRANS FUNDAMENTALS. 2003, E86-A(8):2099-2016.
[59]杨帆,郭立,徐雷等.基于信息量估计法的MIDI音频隐写分析[J].通信技术,2010, 43(1): 86-89.
[60] MMA. General MIDI Specification1.0. http://www.midi.org/techspecs/gm.php.
[61] Roland GS. http://en.wikipedia.org/wiki/Roland_GS.
[62] GM2. http://www.midi.org/techspecs/index.php.
[63]杨军,MIDI消息和标准——MIDI文件格式剖析及应用[J].中南民族大学学报(自然科学版),2003.9
[64] MMA. Standard MIDI-File format Specification1.1.
[65] Nuendo3.音频编辑软件.网站主页: http://www.steinberg.net/en/products/legacyproducts/legacyproducts_nuendo3.html
[66] Qingzhong Liu, Andrew H. Sung, Zhongxue Chen el. Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images [J]. Pattern Recognition 41 (2008) 56–66.
[67] MIDI Database. Free MIDI File Datebase [DB/OL]. http://www.mididb.com/. 2009-8-26.
[68]张海涛,陈宗海,向微.人工神经网络在系统控制和建模中的应用综述[C]. 2003系统仿真技术及其应用学术交流会,上海, 2003-7-1.
[69] Ker A D. Steganalysis of embedding in two least-significant bits [J]. IEEE Trans. on Information and Security, 2007, 2(1):46-54.
[70]赵勇,基于人工神经网络的温度预测在高压气密性检测中的应用研究[D].合肥:中国科学技术大学,2009.
[71] Vapnik V, Levin E, Le C Y.Measuring the VC Dimension of a Learning Machine[J]. Neural Computation, 1994,(6):851-876.
[72]祁亨年.支持向量机及其应用研究[J].计算机工程. 2004, 30(10):6-9.
[73]边肇祺,张学工.模式识别[M].北京:清华大学出版社, 2000.
[74] Zhang Ling. The Relationship Between Kernel Functions Based SVM and Three-layer Feedforward Neural Networks [J].Chinese J.Computer, 2002,25(7):1-5
[75] LIBSVM[EB/OL].网站主页:http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
[76] Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. A Practical Guide to Support Vector Classification[R]. http://www.csie.ntu.edu.tw/~cjlin. May 19 2009.
[77] Kalos Alex. Modeling MIDI Music as Multivariate Time Series[J]. IEEE Congress on Evolutionary Computation. Vancouver Canada. July 16-21, 2006
[78] UltraEdit[EB/OL].网站主页: http://www.ultraedit.com/
[79] LS SVMlab[EB/OL].网站主页: http://www.esat.kuleuven.be/sista/lssvmlab/
[80] OSU SVM[EB/OL].网站主页: http://www.ece.osu.edu/~maj/osu_svm/
[2] KatzenbeiSSer S,petiteola F.信息隐藏技术——隐写术与数字水印[M].吴秋新等译.北京:人民邮电出版社,2001年.
[3]杜江.信息隐藏与数字水印技术研究[D].西安:西安电子科技大学,2001.
[4]孔祥维.信息安全中的信息隐藏理论和方法研究[D].大连:大连理工大学,2003.
[5]张立和等.透视信息隐藏[M].北京:国防工业出版社,2007年.
[6]诸挥明,梁路光,付妍.人耳听阈曲线的测定[J].大学物理实验, 2006, 16(1): 43-46.
[7] BAUER B B , TORICK E L. Researches in loudness measurement [J]. IEEE Transactions on Audio and Electro acoustics, 1966, 3(14):141-151.
[8]袁开国,杨榆,杨义先.音频信息隐藏技术研究[J].中兴通讯技术,2007,13(5):06-09.
[9]谢志文,尹俊勋,饶丹.空间掩蔽效应的实验研究[J].声学学报,2006,31(4):363-369.
[10]曹汉强,张歆昱,向华.一种基于掩蔽效应的数字音频隐藏方案[J],2006,2:42-44.
[11]同鸣,姬红兵,刘晓军.基于相位编码的音频信息隐藏方法[J],2008,34(9):7-9.
[12] Tong Ming, Bian Zhengzhong. Study on Phase Perception in Speech[J]. Journal of Electronics[J], 2003, 20(5): 387-392.
[13]刘子菡,陈小平.人耳听觉对声音相位感知能力的研究[J].世界专业音响和灯光. 2009,10:6-11.
[14]杨正琴,音频隐写分析技术的研究[D],南京:南京理工大学硕士论文,2006年.
[15]吴明巧,数字图像信息隐写与隐写分析技术研究[D],长沙:国防科技大学博士论文,2007年.
[16]刘秀娟,郭立,邱天.改进的大容量多分辨率LSB音频隐写算法[J].计算机工程与应用. 2006, 30:23-25.
[17] Chen N, Zhu J. Blind multiple watermarking algorithm for audio [J]. Journal of Southeast University (English Edition). 2007, 23(2):207-210.
[18]赵朝阳,刘振华,王挺.数字音频信号的回声数据隐藏技术[J].计算机应用研究. 2000,7:42-44.
[19]黄昊,胡涛.一种新的自适应扩频数字音频水印[J]. 2007, 26(4):723-727.
[20] Kirovski D, Malvar H S. Spread-Spectrum Watermarking of Audio Signals [J]. IEEE Transactions on Signal Processing,2003, 51( 4) : 1020-1033.
[21]陈铭,张汝,刘凡凡等.基于DCT域QIM的音频信息伪装算法[J].通信学报. 2009, 30(8):105-111.
[22]马冀平,韩纪庆. DCT域音频水印嵌人对策和算法[J].电子学报,2006, 34(7): 1260-1264.
[23]高海英.基于Huffman编码的MP3隐写算法[J].中山大学学报(自然科学版),2007,46(4):32-35.
[24]陈庆元,刘振华,吕述望.基于载体结构特征的隐写分析[J].计算机工程与应用. 2004, 40(34):39-41.
[25] Pedram H, Vidyasagar P, and Elizabeth C. A Survey of Steganographic and Steganalytic Tools for the Digital Forensic Investigator [EB/OL]. http://debii.curtin.edu.au/~pedram/images/docs/survey_of_steganography_and_steganalytic_tools.pdf. July 2007.
[26]葛秀慧,田浩,郭立甫等,信息隐藏原理及应用[M],北京:清华大学出版社,2008.10.
[27] Petitcolas FAP, Anderson RJ, Kuhn MG. Information hiding-A survey[C]. Proceedings of the IEEE, 1999,87(7):1062~1078.
[28]杨帆. MIDI音频隐写分析研究[D].合肥:中国科学技术大学硕士论文,2009年.
[29] Westfeld A, Ptzmann A. Attacks on Steganographic Systems[C]. Proc.of the 3rd International Workshop on Information Hiding. Dresden, Germany,1999.
[30] Fridrich J,Goljan M,Du R.Reliable Detection of LSB Steganography in Grayscale and Color Images[C]. Proc.of ACM, Special Session on Multimedia Security and Watermarking. Ottawa, Canada,2001.
[31] Dumitrescu S, Wu Xiaolin, Memon N. On Steganalysis of Random LSB Embedding incontinous-tone Images[C]. Proc.of IEEE ICIP’02.New York,USA,2002.
[32] Dumitrescu S,Wu Xiaolin,Wang Zhe.Detection of LSB Steganography via Sample Pair Analysis[C]. Proc.of the 5th Inter-national Workshop on Information Hiding. Springer-Verlag, 2002.
[33] Zhang Tao,Ping Xijian. Reliable Detection of LSB Steganography Based on the Difference Image Histogram[C]. Proc.of IEEE Inter-national Conference on Acoustics,Speech,and Signal Processing.Hong Kong,China,2003.
[34] Zhang XP, Wang SZ, Zhang KW. Steganography with Least Histogram Abnormality[J]. Computer Network Security, Lecture Notes in Computer Science, Springer-Verlag, 2003,2776(2): 395-406
[35] Li Xiaolong, Yang Bin, Cheng Daofang, et al. A Generalization of LSB Matching [J]. IEEE Signal Processing Letters, 2009(16):69-72.
[36] Andrew D.Ker. Steganalysis of LSB Matching in Grayscale Images [J]. IEEE Signal Processing Letters,2005(12):441-444.
[37] Zhang Jun, Cox Ingemar, Doerr Gwenael. Steganalysis for LSB Matching in Images withHigh-frequency Noise [J]. Proceedings of the IEEE Workshop on Multimedia Signal Processing, 2007(10):385-388.
[38] Fridrich J.Soukal D.Goljan M.Maximum likelihood estimation of length of secret message embedded using士K steganography in spatial domain. SPIE 568 1 : Security , Steganography,and Watermarking of Multimedia Contents. San Jose:SPIE Press,2005:595-606
[39]田源,程义民,钱振兴等.基于PCA及SVM的图像信息隐藏检测[J].中国科学院研究生院学报. 2008,25(1):74-79.
[40]杨榆,雷敏,钮心祈等.基于回声隐藏的VDSC隐写分析算法[J].通信学报. 2009, 30(2):83-88.
[41]汪云路,程义民,谢春辉等.基于统计特征的语言回声隐藏分析[J].数据采集与处理.2009,24(4):408-412.
[42]黄昊,郭立,李琳.基于失真测度的直接扩频音频隐写分析[J].中国科学院研究生院学报. 2008,25(2):251-256.
[43]马懿,张政宝,冯帆等.基于小波奇异性的扩频隐写检测算法[J].计算机工程. 2009, 35(15):159-161.
[44]张飞,平西建,张涛.基于DCT系数值对差异的JSteg隐写嵌入率估计法[J].数据采集与处理. 2009,24(2):154-158.
[45]王舰,汤光明.基于冗余零系数消除的F5改进算法[J].计算机工程. 2009, 35(16):164-166.
[46] J. Fridrich. Feature-based Steganalysis for JPEG images and its implications for future design of stegano-graphic schemes[C]. Information Hiding, 6th International Workshop, volume 3200 of Lecture Notes in Computer Science, pages 67–81, 2005.
[47] Mengyu Qiao Sung, A.H. Qingzhong Liu. Feature Mining and Intelligent Computing for MP3 Steganalysis. Bioinformatics[C], Systems Biology and Intelligent Computing, 2009. IJCBS '09.
[48] Fung G,Mangasarian OL. Increment support vector machine classification[R]. Wisconsin: University of Wisconsin ,2001.
[49] McCallum A, Nigam K. A Comparison of Event Models for Naive Bayes Text Classification[J]. Learning for Text Categorization: Papers from the AAAI Workshop. WS- 98- 05, 1998:41- 48.
[50] Nigam K, Lafferty J, McCallum A. Using maximum entropy for text classification [J]. In: IJCAI99 Workshop on Machine Learning for Information Filtering. 1999: 61- 67.
[51] Yang Y, Chut C. An Example-Based Mapping Method for Text Classification and Retrieval [J]. ACMTransactions on Information Systems, 1994, 23(3): 252- 277.
[52] Vapnik V N. The Nature of Statistical Learning Theory[M], NY: Springer?Verlag, 1995张学工译.统计学习理论的本质.北京:清华大学出版社,1999.
[53]邓诗智,刘九芬,张卫明.隐写分析算法的量化评估方案[J].东南大学学报(自然科学版). 2007,37(Ⅰ):76-80.
[54] Alexander Adli, Zensho Nakao. Three Steganography Algorithms for MIDI Files [C]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics. Guangzhou, 2005:18-21.
[55] Matthew W, Beaverton. MIDI File Steganography[P]. United States Patent. No:US7402744B1, Jul.22,2008.
[56] Inoue D, Tsutomu M. A Scheme of Standard MIDI Files Steganography and Its Evaluation[J]. Security and Watermarking of Multimedia Contents IV,2002,4675:194-205
[57] Kotaro Y, Munetoshi I. A Standard MIDI File Steganography Based on Fluctuation of Duration [J]. 2009 International Conference on Availability, Reliability and Security.774-779.
[58] INOUE D, SUZUKI M, MATSUMOTO T. Detection-Resistant Steganography for Standard MIDI Files[J]. IEICE TRANS FUNDAMENTALS. 2003, E86-A(8):2099-2016.
[59]杨帆,郭立,徐雷等.基于信息量估计法的MIDI音频隐写分析[J].通信技术,2010, 43(1): 86-89.
[60] MMA. General MIDI Specification1.0. http://www.midi.org/techspecs/gm.php.
[61] Roland GS. http://en.wikipedia.org/wiki/Roland_GS.
[62] GM2. http://www.midi.org/techspecs/index.php.
[63]杨军,MIDI消息和标准——MIDI文件格式剖析及应用[J].中南民族大学学报(自然科学版),2003.9
[64] MMA. Standard MIDI-File format Specification1.1.
[65] Nuendo3.音频编辑软件.网站主页: http://www.steinberg.net/en/products/legacyproducts/legacyproducts_nuendo3.html
[66] Qingzhong Liu, Andrew H. Sung, Zhongxue Chen el. Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images [J]. Pattern Recognition 41 (2008) 56–66.
[67] MIDI Database. Free MIDI File Datebase [DB/OL]. http://www.mididb.com/. 2009-8-26.
[68]张海涛,陈宗海,向微.人工神经网络在系统控制和建模中的应用综述[C]. 2003系统仿真技术及其应用学术交流会,上海, 2003-7-1.
[69] Ker A D. Steganalysis of embedding in two least-significant bits [J]. IEEE Trans. on Information and Security, 2007, 2(1):46-54.
[70]赵勇,基于人工神经网络的温度预测在高压气密性检测中的应用研究[D].合肥:中国科学技术大学,2009.
[71] Vapnik V, Levin E, Le C Y.Measuring the VC Dimension of a Learning Machine[J]. Neural Computation, 1994,(6):851-876.
[72]祁亨年.支持向量机及其应用研究[J].计算机工程. 2004, 30(10):6-9.
[73]边肇祺,张学工.模式识别[M].北京:清华大学出版社, 2000.
[74] Zhang Ling. The Relationship Between Kernel Functions Based SVM and Three-layer Feedforward Neural Networks [J].Chinese J.Computer, 2002,25(7):1-5
[75] LIBSVM[EB/OL].网站主页:http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
[76] Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. A Practical Guide to Support Vector Classification[R]. http://www.csie.ntu.edu.tw/~cjlin. May 19 2009.
[77] Kalos Alex. Modeling MIDI Music as Multivariate Time Series[J]. IEEE Congress on Evolutionary Computation. Vancouver Canada. July 16-21, 2006
[78] UltraEdit[EB/OL].网站主页: http://www.ultraedit.com/
[79] LS SVMlab[EB/OL].网站主页: http://www.esat.kuleuven.be/sista/lssvmlab/
[80] OSU SVM[EB/OL].网站主页: http://www.ece.osu.edu/~maj/osu_svm/