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Reseach Article

A Multi-Algorithm, High Reliability, Extensible Steganalyzer using Services Oriented Architecture

by Eman Abdelfattah, Ausif Mahmood
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 5
Year of Publication: 2011
Authors: Eman Abdelfattah, Ausif Mahmood
10.5120/3297-4503

Eman Abdelfattah, Ausif Mahmood . A Multi-Algorithm, High Reliability, Extensible Steganalyzer using Services Oriented Architecture. International Journal of Computer Applications. 27, 5 ( August 2011), 18-26. DOI=10.5120/3297-4503

@article{ 10.5120/3297-4503,
author = { Eman Abdelfattah, Ausif Mahmood },
title = { A Multi-Algorithm, High Reliability, Extensible Steganalyzer using Services Oriented Architecture },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 5 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 18-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number5/3297-4503/ },
doi = { 10.5120/3297-4503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:58.971808+05:30
%A Eman Abdelfattah
%A Ausif Mahmood
%T A Multi-Algorithm, High Reliability, Extensible Steganalyzer using Services Oriented Architecture
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 5
%P 18-26
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a unified Steganalyzer that can work with different media types such as images and audios. It is also capable of providing improved accuracy in stego detection through the use of multiple algorithms. The designed system integrates different steganalysis techniques in a reliable Steganalyzer by using a Services Oriented Architecture (SOA). Other contributions of the research done in this paper include, an improved Mel-Cepstrum technique for audio wav files feature extraction that results in better accuracy in stego detection (> 99.9%), improved overall classification system that is based on three individual classifiers; a Neural Network classifier, a Support Vector Machines classifier, and an AdaBoost algorithm based classifier. Finally, an extensible classifier is introduced that allows incorporation of detecting new embedding techniques to the current system, so that the framework will continue to provide reliable stego detection for future embedding algorithms.

References
  1. Fridrich, J.: Feature-based steganalysis for jpeg images and its implications for future design of steganographic schemes. Proc. 6th Information Hiding Workshop, Toronto. 67–81 (2004)
  2. Kharrazi, M., Sencar, H. T., Memon, N.: Benchmarking steganographic and steganalysis techniques. Security, Steganography, and Watermarking of Multimedia Contents VII. Edited by Delp, Edward J., III; Wong, Ping W. Proceedings of the SPIE, vol. 5681. 252-263 (2005)
  3. Martin, A., Sapiro, G., Seroussi, G.: Is image steganography natural? IEEE Transactions on Image Processing, issue 12. 2040 – 2050 December (2005)
  4. Upham, D.: JPEG-JSTEG—Modifications of the Independent JPEG Groups JPEG Software for 1-Bit Steganography in JFIF Output Files. ftp://ftp.funet.fi/pub/crypt/steganography/ Accessed 20 May 2011
  5. Tzschoppe, R., Bäuml, R., Huber, J. B., Kaup, A.: Steganographic system based on higher-order statistics. SPIE Security and Watermarking of Multimedia Contents V, Santa Clara, CA. (2003)
  6. Brown, A.: S-Tools for Windows, 1994. ftp://ftp.ntua.gr/pub/crypt/mirrors/idea.sec.dsi.unimi.it/code/s-tools4.zip Accessed 20 May 2011
  7. Gousseau, Y., Morel, J. M.: Are natural images of bounded variation. SIAM J. Math. Anal., vol. 33, no. 3. 634–648 (2001)
  8. Alvarez, L., Gousseau, Y., Morel, J. M.: The size of objects in natural images. CMLA. Cachan, France: Ecole Normale Sup. (1999)
  9. Grenander, U., Srivastava, A.: Probability models for clutter in natural images. IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 4. 424-429 Apr(2001)
  10. Srivastava, A., Liu, X., Grenander, U.: Universal analytical forms for modeling image probabilities. IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 9. 1200 – 1214 (2002)
  11. Grenander, U.: Toward a theory of natural scenes. Technical report, Brown Univ., Providence, RI, (2003).
  12. Green, M. L.: Statistics of images, the TV algorithm of Rudin-Osher-Fatemi for image denoising and an improved denoising algorithm. Technical Report, Univ. California, Los Angeles, (2002).
  13. Avcibas, I., Memon, N., Sankur, B.: Steganalysis using image quality metrics. Proc. Security and Watermarking of Multimedia Contents, San Jose, CA. 523 – 531 (2001)
  14. Avcibas, I., Kharrazi, M., Memon, N., Sankur, B.: Image steganalysis with binary similarity measures. EURASIP J. Appl. Signal Process, vol. 17. 2749–2757 (2005)
  15. Farid, H.: Detecting hidden messages using higher-order statistical models. Proc. IEEE International Conference on Image Processing (ICIP ’02), vol. 2, Rochester, NY, USA. 905–908 September (2002)
  16. Provos, N. Outguess. http://www.outguess.org/ detection.php Accessed 20 May 2011
  17. Westfeld, A.: F5-A Steganographic Algorithm: high capacity despite better steganalysis. Proceedings of the 4th International Workshop on Information Hiding, Lecture Notes In Computer Science; vol. 2137. 289 - 302 (2001)
  18. Sallee, P.: Model-based steganography. Proc. Int. Workshop on Digital Watermarking, Seoul, Korea. 254-260 (2003)
  19. Fridrich, J., Goljan, M., Soukal, D.: Perturbed quantization steganography with wet paper codes. Proc. ACM Multimedia Workshop, Magdeburg, Germany. 4–15 (2004)
  20. Kharrazi, M., Sencar, H. T., Memon, N.: Performance study of common image steganography and steganalysis techniques. Journal of Electronic Imaging vol. 15, issue 4. Oct–Dec (2006)
  21. Lyu, S., Farid, H.: Detecting hidden messages using higher-order statistics and support vector machines. Proc. 5th Int. Workshop on Information Hiding. 340-354 (2002)
  22. Lyu, S., Farid, H.: Steganalysis using color wavelet statistics and one-class support vector machines. Proc. SPIE 5306. 35-45 (2004)
  23. Goljan, M., Fridrich, J., Holotyak, T.: New Blind Steganalysis and its Implications. Proc. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII, vol. 6072, San Jose. CA. 1-13 January (2006)
  24. Holotyak, T., Fridrich, Voloshynovskiy, J. S.: Blind Statistical Steganalysis of Additive Steganography Using Wavelet Higher Order Statistics. 9th IFIP TC-6 TC-11 Conference on Communications and Multimedia Security, LNCS vol. 3677, Springer-Verlag, Berlin. 273–274 (2005)
  25. Tian, H., Zhou, K., Jiang, H., Liu, J., Huang, Y., Feng, D.: An M-Sequence Based Steganography Model for Voice over IP. ICC '09. IEEE International Conference on Communications. 1-5 August (2009)
  26. Liu, Q., Sung, A. H., Qiao, M.: Temporal Derivative-Based Spectrum and Mel-Cepstrum Audio Steganalysis. IEEE Transactions on Information Forensics and Security, vol. 4, no. 3. 359-368 September (2009)
  27. Tian, H., Zhou, K., Jiang, H., Liu, J., Huang, Y., Feng, D.: An adaptive Steganography Scheme for Voice over IP. The 2009 IEEE International Symposium on Circuits and Systems. 2922-2925 (2009)
  28. Liu, Q., Sung, A. H., Qiao, M.: Novel Stream Mining for Audio Steganalysis. Proceedings of the seventeen ACM international conference on Multimedia, Beijing, China. 95 – 104 October (2009)
  29. Qiao, M., Sung, A. H., Liu, Q.: Feature Mining and Intelligent Computing for MP3 Steganalysis. 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing. 627-630 (2009)
  30. Introduction to Degol. http://wandership.ca/ projects/deogol/intro.html Accessed 20 May 2011
  31. Li, Z., Sun, X., Wang, B., Wang, X.: A Steganography Scheme in P2P Network. IIHMSP '08 International Conference on Intelligent Information Hiding and Multimedia Signal Processing. 20 – 24 Aug (2008)
  32. Lalbakhsh, P., Ravanbakhsh, S., Fesharaki, M. N., Sohrabi, N.: Service Oriented Steganography - A novel approach towards autonomic secured distributed heterogeneous environments. 2009 International Conference on Signal Processing Systems, Singapore. 418 - 422 (2009)
  33. Papazoglou, M. P., Heuvel, W.: Service oriented architectures: approaches, technologies and research issues. The VLDB Journal — The International Journal on Very Large Data Bases, Springer-Verlag New York, Inc., vol. 16, issue 3. 389 - 415 July (2007)
  34. MacKenzie, C. M., Laskey, K., McCabe, F., Brown, P. F., Metz, R.: OASIS Reference Model for Service Oriented Architecture 1.0. Organization for the Advancement of Structured Information Standards (OASIS), Committee Specification 1. August (2006) http://www.oasis-open.org/committees/download.php/19679/soa-rm-cs.pdf Accessed 20 May 2011
  35. Sprott, D., Wilkes, L. Understanding Service-Oriented Architecture. http://msdn.microsoft.com/en-us/library/aa480021.aspx Accessed 20 May 2011
  36. He, H. What Is Service-Oriented Architecture. http://www.xml.com/pub/a/ws/2003/09/30/soa.html Accessed 20 May 2011
  37. Liu, Q., Sung, A. H., Qiao, M.: Derivative Based Audio Steganalysis. ACM Transactions on Multimedia Computing, Communications and Applications, in press.
  38. Gonzalez, R., Woods, R.: Digital Image Processing. 3rd edition, Prentice Hall (2008)
  39. Liu, Q., Sung, A. H., Qiao, M.: Improved Detection and Evaluation for JPEG Steganalysis. Proceedings of the seventeen ACM international conference on Multimedia, Beijing, China. 873-876 October (2009)
  40. Abdelfattah, E., Mahmood, A. A Multi-Algorithm, High Reliability Steganalyzer based on Services Oriented Architecture. International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering December (2010)
  41. Ellis, D.: PLP and RASTA (and MFCC, and inversion) in Matlab using melfcc.m and invmelfcc.m. http://labrosa.ee.columbia.edu/matlab/rastamat/ Accessed 20 May 2011
  42. Freund, Y., Shapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Proceedings of the Second European Conference on Computational Learning Theory. 23 – 37 (1995)
  43. Rojas, R.: AdaBoost and the Super Bowl of Classifiers A Tutorial Introduction to Adaptive Boosting. (2009)
  44. Zilouchian, A., Jamshidi, M.: Intelligent Control Systems Using Soft Computing Methodologies. Chapter 2, Fundamentals of Neural Networks, CRC Press. (2001)
  45. Liu, Q. http://www.cs.nmt.edu/~liu/downloads.html Accessed 20 May 2011
  46. Steghide. http://steghide.sourceforge.net/ Accessed 20 May 2011
  47. Quach,T.: Information Similarity Metrics in Information Security and Forensics. Ph.D. Dissertation, University of New Mexico, Albuquerque. (2009)
  48. Invisible Secrets 4. http://www.invisiblesecrets.com/ Accessed 20 May 2011
  49. Mertayak, C. AdaBoost. http://www.mathworks.com/ matlabcentral/fileexchange/21317-adaboost Accessed 20 May 2011
Index Terms

Computer Science
Information Sciences

Keywords

Mel-Cepstrum Support Vector Machines Neural Networks AdaBoost