Volume 7 Number 4
November 2010
Article Contents
S. Geetha, Siva S. Sivatha Sindhu and N. Kamaraj. Passive Steganalysis Based on Higher Order Image Statistics of Curvelet Transform. International Journal of Automation and Computing, vol. 7, no. 4, pp. 531-542, 2010. doi: 10.1007/s11633-010-0537-1
Cite as: S. Geetha, Siva S. Sivatha Sindhu and N. Kamaraj. Passive Steganalysis Based on Higher Order Image Statistics of Curvelet Transform. International Journal of Automation and Computing, vol. 7, no. 4, pp. 531-542, 2010. doi: 10.1007/s11633-010-0537-1

Passive Steganalysis Based on Higher Order Image Statistics of Curvelet Transform

  • Received: 2009-02-11
  • Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye.This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem.A critical part of the steganalyser design depends on the selection of informative features.This paper is aimed at proposing a novel attack with improved performance indices with the following implications:1) employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images,as compared to other conventional wavelet transforms;2) increasing the sensitivity and specificity of the system by the feature reduction phase;3) realizing the system using an efficient classification engine,a neuro-C4.5 classifier,which provides better classification rate.An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.
  • 加载中
  • [1] F.A.P.Petitcolas,R.J.Anderson,M.G.Kuhn.Informa-tion hiding—A survey.In Proceedings of IEEE,vol.87, no.7,pp.1062-1078,1999.
    [2] S.Katzenbeisser,F.A.P.Petitcolas.Information Hiding Techniques for Steganography and Digital Watermarking, Norwood,MA,USA:Artech House,2000.
    [3] W.Bender,D.Gruhl,N.Morimot,A.Lu.Techniques for data hiding.IBM Systems Journal,vol.35,no.4,pp.313-336,1996.
    [4] N.Nikolaidis,I.Pitas.Robust image watermarking in the spatial domain.Signal Processing,vol.66,no.3,pp.385-403,1998.
    [5] Y.K.Lee,L.H.Chen.High capacity image steganographic model.IEE Proceedings:Vision,Image and Signal Process-ing,vol.147,no.3,pp.288-294,2000.
    [6] W.N.Lie,G.S.Lin,C.L.Wu.Robust image watermarking on the DCT domain.In Proceedings of IEEE International Symposium on Circuits and Systems,Geneva,Switzerland, vol.1,pp.228-231,2000.
    [7] J.Huang,Y.Q.Shi.Adaptive image watermarking scheme based on visual masking.Electronics Letters,vol.34,no.8, pp.748-750,1998.
    [8] T.Ogihara,D.Nakamura,N.Yokoya.Data embedding into pictorial with less distortion using discrete cosine trans-form.In Proceedings of International Conference on Pat-tern Recognition,Vienna,Austria,pp.675-679,1996.
    [9] I.J.Cox,J.Kilian,F.T.Leighton,T.Shamoon.Se-cure spread spectrum watermarking for multimedia.IEEE Transactions on Image Processing,vol.6,no.12,pp.1673-1687,1997.
    [10] Q.Cheng,T.S.Huang.An additive approach to transform-domain information hiding and optimum detection struc-ture.IEEE Transactions on Multimedia,vol.3,no.3, pp.273-284,2001.
    [11] C.I.Podilchuk,W.Zeng.Image-adaptive watermarking us-ing visual models.IEEE Journal on Selected Areas in Com-munications,vol.16,no.4,pp.525-539,1998.
    [12] X.Y.Wang,J.Wu.A feature-based robust digital image watermarking against desynchronization attacks.Interna-tional Journal of Automation and Computing,vol.4,no.4, pp.428-432,2007.
    [13] B.Xu,Z.B.Zhang,J.Z.Wang,X.Q.Liu.Improved BSS based schemes for Active steganalysis.In Proceed-ings of ACIS International Conference on Software Engi-neering,Artificial Intelligence,Networking and Parallel Dis-tributed Computing,IEEE,Qingdao,PRC,vol.3,pp.815-818,2007.
    [14] J.Fridrich.Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes.In Proceedings of International Workshop on In-formation Hiding,Lecture Notes in Computer Science, Springer,vol.3200,pp.67-81,2005.
    [15] S.Geetha.[J].S.S.Sivatha Sindhu,N.Kamaraj.Blind im-age steganalysis based on content independent statistical measures maximizing the specificity and sensitivity of the system.Computers&Security,vol.28,no.7,pp.683-69.2008,:-
    [16] S.Geetha,S.S.Sivatha Sindhu,N.Kamaraj.Close color pair signature ensemble adaptive threshold based steganal-ysis for LSB embedding in digital images.Transactions on Data Privacy,vol.1,no.3,pp.140-161,2008.
    [17] S.Geetha,S.S.S.Sindhu,N.Kamaraj.Steganalysis of LSB embedded images based on adaptive threshold close color pair signature.In Proceedings of the 6th IEEE In-dian Conference on Computer Vision,Graphics and Image Processing,IEEE,pp.281-288,2008.
    [18] S.Geetha,S.S.S.Sindhu,N.Kamaraj.StegoBreaker: Defeating the steganographic systems through genetic-X-means approach using image quality metrics.In Proceed-ings of the 16th IEEE International Conference on Ad-vanced Computing and Communication,IEEE,pp.382-391,2008.
    [19] S.Geetha,S.S.S.Sindhu,N.Kamaraj.StegoCracker:A genetic algorithm tuned neural network paradigm for break-ing the audio steganographic utilities.In Proceedings of IEEE Indicon,pp.180-186,2007.
    [20] J.Fridrich.[J].M.Goljan.Practical steganalysis of digital im-ages—State of the art.In Proceedings of the SPIE Inter-national Conference on Security and Watermarking of Mul-timedia Contents,San Jose,CA,USA,vol.4675,pp.1-1.2002,:-
    [21] C.Manikopoulos,Y.Q.Shi,S.Song,Z.Zhang,Z.Ni,D. Zou.Detection of block DCT-based steganography in gray-scale images.In Proceedings of the 5th IEEE Workshop on Multimedia Signal Processing,IEEE,pp.355-358,2002.
    [22] R.Chandramouli.A mathematical approach to steganaly-sis.In Proceedings of the SPIE International Conference on Security and Watermarking of Multimedia Contents,San Jose,CA,USA,vol.4675,pp.14-25,2002.
    [23] J.J.Harmsen,W.A.Pearlman.Steganalysis of additive noise modelable information hiding.In Proceedings of the SPIE,vol.5020,pp.131-142,2003.
    [24] I.Avcibas,N.Memon,B.Sankur.Steganalysis using image quality metrics.IEEE Transactions on Image Processing, vol.12,no.2,pp.221-229,2003.
    [25] W.N.Lie,G.S.Lin.A feature-based classification tech-nique for blind image steganalysis.IEEE Transactions on Multimedia,vol.7,no.6,pp.1007-1020,2005.
    [26] J.J.Harmsen.Steganalysis of Additive Noise Modelable Information Hiding,Master dissertation,Rensselaer Poly-technic Institute,Troy,New York,USA,2003.
    [27] T.Holotyak,J.Fridrich,S.Voloshynovskiy.Blind statis-tical steganalysis of additive steganography using wavelet higher order statistics.Lecture Notes in Computer Science, Springer,pp.273-274,2005.
    [28] Y.Q.Shi,G.Xuan,C.Yang,J.Gao,Z.Zhang,P.Chai, D.Zou,C.Chen,W.Chen.E?ective steganalysis based on statistical moments of wavelet characteristic function. In Proceedings of IEEE International Conference on Infor-mation Technology:Coding and Computing,IEEE,vol.1, pp.768-773,2005.
    [29] E.J.Candes,D.L.Donoho.New tight frames of curvelets and optimal representations of objects with C2 singulari-ties.Communications on Pure and Applied Mathematics, vol.57,no.2,pp.219-266,2004.
    [30] R.R.Coifman,D.L.Donoho.Translation-invariant denois-ing.Lecture Notes in Statistics,Springer,vol.103,pp.125-150,1995.
    [31] J.L.Starck,E.J.Candes,D.L.Donoho.The curvelet transform for image denoising.IEEE Transactions on Im-age Processing,vol.11,no.6,pp.670-684,2001.
    [32] E.J.Candes,D.L.Donoho,C.R.A.Cohen,L.L. Schumaker.Curvelets—A surprisingly effective nonadap-tive representation for objects with edges.Curves Surfaces, Nashville,TN,USA,pp.105-120,2000.
    [33] N.Kingsbury,T.Reves.Redundant representation with complex wavelets:How to achieve sparsity.In Proceedings of International Conference on Image Processing,IEEE, Barcelona,Spain,vol.1,pp.45-48,2003.
    [34] Z.H.Zhou,J.Wu,W.Tang.Ensembling neural net-works:Many could be better than all.Artificial Intelligence, vol.137,no.1-2,pp.239-263,2002.
    [35] J.R.Quinlan.C4.5:Programs for Machine Learning,San Mateo,CA,USA:Morgan Kaufmann,1993.
    [36] Z.H.Zhou,Z.Q.Chen.Hybrid decision tree.Knowledge-based Systems,vol.15,no.8,pp.515-528,2002.
    [37] Z.H.Zhou,Y.Jiang.NeC4.5:Neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering, vol.16,no.6,pp.770-773,2004.
    [38] B.Efron,R.Tibshirani,R.J.Tibshirani.An Introduction to the Bootstrap,New York,USA:Chapman&Hall,1993.
    [39] L.Breiman.Bagging predictors.Machine Learning,vol.24, no.2,pp.123-140,1996.
    [40] PictureMarc,Embed Watermark,v 1.00.45,Digimarc Corp.
    [41] M.Kutterand,F.Jordan.JK-PGS(Pretty Good Signature),Signal Processing Laboratory at Swiss Federal Institute of Technology(EPFL),Lausanne, Switzerland,[Online] ,Available:http://ltswww. epfl.ch/?kutter/watermarking/JK PGS.html,1998.
    [42] I.J.Cox,J.Kilian,F.T.Leighton,T.Shamoon.Se-cure spread spectrum watermarking for multimedia.IEEE Transactions on Image Processing,vol.6,no.12,pp.1673-1687,1997.
    [43] A.Brown.S-tools Version 4.0,[Online] ,Available:http:// members.tripod.com/steganography/stego/s-tools4.html.
    [44] Steganos Security Suite,[Online] ,Available:http:// www.steganos.com/english/steganos/download.htm.
    [45] J Korejwa.Shell 2.0,[Online] ,Available:http://www. tiac.net/users/korejwa/steg.htm.
    [46] Images.[Online] ,Available:http://www.cl.cam.ac.uk/? fapp2/watermarking/benchmark/image database.html.
    [47] J.H.Holland.Adaptation in Natural and Artificial Sys-tems,University of Michigan Press,1975.
    [48] NeC45.zip,[Online] ,Available:http://lamda.nju.edu.cn/ datacode/NeC4.5/nec45.zip.
  • 加载中
  • [1] Wei-Ning Wang, Qi Li, Liang Wang. Robust Object Tracking via Information Theoretic Measures . International Journal of Automation and Computing, 2020, 17(): 1-15.  doi: 10.1007/s11633-020-1235-2
    [2] Biao Chang, Tong Xu, Qi Liu, En-Hong Chen. Study on Information Diffusion Analysis in Social Networks and Its Applications . International Journal of Automation and Computing, 2018, 15(4): 377-401.  doi: 10.1007/s11633-018-1124-0
    [3] Jhon A. Isaza, Hector A. Botero, Hernan Alvarez. State Estimation Using Non-uniform and Delayed Information: A Review . International Journal of Automation and Computing, 2018, 15(2): 125-141.  doi: 10.1007/s11633-017-1106-7
    [4] Santosh Kumar Vipparthi, ShyamKrishna Nagar. Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System . International Journal of Automation and Computing, 2016, 13(5): 457-467.  doi: 10.1007/s11633-016-0978-2
    [5] Dang-Dang Niu, Lei Liu, Xin Zhang, Shuai Lü, Zhuang Li. Security Analysis Model, System Architecture and Relational Model of Enterprise Cloud Services . International Journal of Automation and Computing, 2016, 13(6): 574-584.  doi: 10.1007/s11633-016-1014-2
    [6] Syeda Mariam Muzammal, Munam Ali Shah, Si-Jing Zhang, Hong-Ji Yang. Conceivable Security Risks and Authentication Techniques for Smart Devices: A Comparative Evaluation of Security Practices . International Journal of Automation and Computing, 2016, 13(4): 350-363.  doi: 10.1007/s11633-016-1011-5
    [7] Hui Guan,  Hongji Yang,  Jun Wang. An Ontology-based Approach to Security Pattern Selection . International Journal of Automation and Computing, 2016, 13(2): 168-182.  doi: 10.1007/s11633-016-0950-1
    [8] Xiao-E Ruan,  Zhao-Zhen Li,  Z. Z. Bien. Discrete-frequency Convergence of Iterative Learning Control for Linear Time-invariant systems with Higher-order Relative Degree . International Journal of Automation and Computing, 2015, 12(3): 281-288.  doi: 10.1007/s11633-015-0884-z
    [9] Sepehr Keykhaie,  Saleh Yousefi,  Mehdi Dehghan. Modeling of Propagation of Road Hazard Information in Sparse Vehicular ad hoc Networks . International Journal of Automation and Computing, 2015, 12(5): 518-528.  doi: 10.1007/s11633-014-0860-z
    [10] Shuang Huang,  Chun-Jie Zhou,  Shuang-Hua Yang,  Yuan-Qing Qin. Cyber-physical System Security for Networked Industrial Processes . International Journal of Automation and Computing, 2015, 12(6): 567-578.  doi: 10.1007/s11633-015-0923-9
    [11] R. I. Minu,  K. K. Thyagharajan. Semantic Rule Based Image Visual Feature Ontology Creation . International Journal of Automation and Computing, 2014, 11(5): 489-499.  doi: 10.1007/s11633-014-0832-3
    [12] Fan Guo, Jin Tang, Zi-Xing Cai. Image Dehazing Based on Haziness Analysis . International Journal of Automation and Computing, 2014, 11(1): 78-86.  doi: 10.1007/s11633-014-0768-7
    [13] P. K. Parlewar, K. M. Bhurchandi. A 4-quadrant Curvelet Transform for Denoising Digital Images . International Journal of Automation and Computing, 2013, 10(3): 217-226.  doi: 10.1007/s11633-013-0715-z
    [14] Methods for Controlling the Authenticity of Textual Information Transfer on the Basis of Statistical and Structural Redundancy . International Journal of Automation and Computing, 2012, 9(5): 518-529.  doi: 10.1007/s11633-012-0675-8
    [15] Chang-Jiang Zhang, Bo Yang. A Novel Nonlinear Algorithm for Typhoon Cloud Image Enhancement . International Journal of Automation and Computing, 2011, 8(2): 161-169.  doi: 10.1007/s11633-011-0569-1
    [16] Song Xu, Xiao-Rong Hou. A Family of Adaptive H Controllers with Full Information for Dissipative Hamiltonian Systems . International Journal of Automation and Computing, 2011, 8(2): 209-214.  doi: 10.1007/s11633-011-0575-3
    [17] Biliana Alexandrova-Kabadjova, Edward Tsang, Andreas Krause. Market Structure and Information in Payment Card Markets . International Journal of Automation and Computing, 2011, 8(3): 364-370.  doi: 10.1007/s11633-011-0593-1
    [18] Tetsuro Morimoto, Tohru Mihashi, Katsushi Ikeuchi. Color Restoration Method Based on Spectral Information Using Normalized Cut . International Journal of Automation and Computing, 2008, 5(3): 226-233.  doi: 10.1007/s11633-008-0226-5
    [19] Shang-Ming Zhou, John Q. Can, Li-Da Xu, Robert John. Interactive Image Enhancement by Fuzzy Relaxation . International Journal of Automation and Computing, 2007, 4(3): 229-235.  doi: 10.1007/s11633-007-0229-7
    [20] Woong Choi, Tadao Isaka, Mamiko Sakata, Seiya Tsuruta, Kozaburo Hachimura. Quantification of Dance Movement by Simultaneous Measurement of Body Motion and Biophysical Information . International Journal of Automation and Computing, 2007, 4(1): 1-7.  doi: 10.1007/s11633-007-0001-z
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Abstract Views (3674) PDF downloads (3095) Citations (0)

Passive Steganalysis Based on Higher Order Image Statistics of Curvelet Transform

Abstract: Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye.This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem.A critical part of the steganalyser design depends on the selection of informative features.This paper is aimed at proposing a novel attack with improved performance indices with the following implications:1) employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images,as compared to other conventional wavelet transforms;2) increasing the sensitivity and specificity of the system by the feature reduction phase;3) realizing the system using an efficient classification engine,a neuro-C4.5 classifier,which provides better classification rate.An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.

S. Geetha, Siva S. Sivatha Sindhu and N. Kamaraj. Passive Steganalysis Based on Higher Order Image Statistics of Curvelet Transform. International Journal of Automation and Computing, vol. 7, no. 4, pp. 531-542, 2010. doi: 10.1007/s11633-010-0537-1
Citation: S. Geetha, Siva S. Sivatha Sindhu and N. Kamaraj. Passive Steganalysis Based on Higher Order Image Statistics of Curvelet Transform. International Journal of Automation and Computing, vol. 7, no. 4, pp. 531-542, 2010. doi: 10.1007/s11633-010-0537-1
Reference (48)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return