Universal statistical steganalytic method

Broda, Martin; Hajduk, Vladimír; Levický, Dušan

doi:10.1515/jee-2017-0016

Abstract

Novel image steganalytic method used to detection of secret message in static images is introduced in this paper. This method is based on statistical steganalysis (SS), where statistical vector is composed by 285 statistical features (parameters) extracted from DCT (Discrete Cosine Transformation) domain and 46 features extracted mainly from DWT (Discrete Wavelet Transformation) domain. Classification process was realized by Ensemble classifier that was helpful in reduction of computational and time complexity. Proposed steganalytic method was verified by detection of popular image steganographic methods. Novel method was also compared with existing steganalytic methods by overall detection accuracy of a secret message.

References

[1] A. Westfeld, ”F5-A Steganographic algorithm: High capacity despite better steganalysis”, Proceedings of the 4th International Workshop on Information Hiding, New York Springer-Verlag, 2001, pp. 289-302. ISBN 3-540-42733-.10.1007/3-540-45496-9_21
Search in Google Scholar Back to article
[2] J. Fridrich, M. Goljan and D. Soukal, ”Perturbed quantization steganography”, Multimedia Systems Journal, 2005, vol. 11, no. 2, pp. 98-107. DOI 10.1007/s00530-005-0194-.10.1007/s00530-005-0194-3
Search in Google Scholar Back to article
[3] P. Sallee, ”Model-based methods for steganography and steganalysis”, International Journal of Image and Graphics, 2005, vol. 5, no. 1, pp. 167-190. ISSN 1793-675.
Search in Google Scholar Back to article
[4] N. Provos, ”Defending against statistical steganalysis”, Proceedings of the 10th USENIX Security Symposium, Washington, DC USENIX Association Berkeley, 2001. ISBN 978-1-931971-23.
Search in Google Scholar Back to article
[5] G. Xuan et al ”JPEG Steganalysis based on classwise non-principal components analysis and multi-directional Markov model”, Proceedings of the 2007 IEEE International Conference on Mul- timedia and Expo, pp. 903-906. DOI 10.1109/ICME.2007.4284 79.10.1109/ICME.2007.4284797
Search in Google Scholar Back to article
[6] G. Xuan et al ”A novel pattern classification scheme: class- wise non-principal component analysis (CNPCA)”, 18th International Conference on Pattern Recognition (ICPR), Hong Kong, 2006, vol. 3, pp. 320-323. DOI 10.1109/ICPR.2006.14.
Search in Google Scholar Back to article
[7] Y. Q. Shi et al ”Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network”, Proceedings of the 2005 IEEE In- ternational Conference on Multimedia and Expo, Amsterdam, Netherlands. DOI 10.1109/ICME.2005.1521412.
Search in Google Scholar Back to article
[8] R. Bohme, ”Advanced statistical Steganalysis”, Dresden, Springer 2009. ISBN 978-3-642-14312-.
Search in Google Scholar Back to article
[9] J. Fridrich, M. Goljan and D. Hogea, ”Steganalysis of JPEG images, Breaking the F5 algorithm”, Information Hiding, 5th International Workshop, vol. 2578 of Lecture Notes Computer Science, pp. 310-323, Noordwijkerhout, Netherlands Springer-Verlag, 2002. DOI 10.1007/3-540-36415-3 2.
Search in Google Scholar Back to article
[10] I. Avcibas, M. Kharrazi, N. Memon and B. Sankur, ”Image Steganalysis with binary similarity measures”, EURASIP Journal on Advances Signal Processing, 2005. DOI 10.1155/ASPP. 2005.274.10.1155/ASP.2005.2749
Search in Google Scholar Back to article
[11] S. R. Gunn, ”Support Vector Machines for Classification and Regression”, Southampton. University of Southampton, Faculty of Engineering, Science and Mathematics, 1998. Available at http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pd.
Search in Google Scholar Back to article
[12] V. Bnoci, M. Broda, G. Bugr and D. Levick, ”Universal Image Steganalytic Method”, Radioengineering Journal, vol. 23, no. 4, pp. 1213-1220, 2014. ISSN 1210-251.
Search in Google Scholar Back to article
[13] G. Bugár, V. Bánoci, M. Broda and D. Levick´y, ”A Novel Approach for Image Steganalysis”, Proceedings of ELMAR-2014 56th International Symposium, Zadar, Croatia, Zagreb University of Zagreb, Faculty of Electrical Engineering and Computing, 2014, pp. 171-174. ISBN 978-953-184-199-.10.1109/ELMAR.2014.6923344
Search in Google Scholar Back to article
[14] Z. Wang and A. C. Bovik, ”Mean Squared Error: Love It or Leave It? A new look at signal fidelity measures”, IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98-117, 2009. DOI 10.1109/MSPP. 2008.93064.10.1109/MSP.2008.930649
Search in Google Scholar Back to article
[15] J. Kodovsk´y, J. Fridrich and V. Holub, ”Ensemble Classifiers for Steganalysis of Digital Media, IEEE Transactions on Information Forensics and Security, vol”, 7, no. 2, pp. 432-444, 2012. DOI 10.1109/TIFS.2011.217591.10.1109/TIFS.2011.2175919
Search in Google Scholar Back to article
[16] M. Welling, ”Fisher Linear Discriminant Analysis”, Department of Computer Science, University of Toronto. Available at http://www.ics.uci.edu/welling/classnotes/papersclass/Fisher-LDA.pd.
Search in Google Scholar Back to article
[17] A. Nag, S. Biswas, D. Sarkar and P. P. Sarkar, ”A Novel Tech- nique for Image Steganography Based on DWT and Huffman Encoding”, International Journal of Computer Science and Se- curity (IJCSS), 2011, vol. 4, no. 6, pp. 561-570. ISSN 1985-155.
Search in Google Scholar Back to article
[18] M. Broda, V. Hajduk and D. Levick´y, ”Image Steganography Based on Combination of YCbCr Color Model and DWT”, Proceedings of ELMAR-2014 57th International Symposium, Zadar, Croatia, Zagreb University of Zagreb, Faculty of Elec- trical Engineering and Computing, 2015, pp. 201-204. ISBN 978-953-184-209-.
Search in Google Scholar Back to article
[19] M. Ashraf and M. Mahmoud, ”Performance Evaluation of Different Universal Steganalysis Techniques JPG Files”, Annales UMCS, Informatica, 2013, vol. 12, no. 3, pp. 121-139, ISSN (On- line) 2083-3628, ISSN (Print) 1732-136.10.2478/v10065-012-0026-y
Search in Google Scholar Back to article

Universal statistical steganalytic method

Abstract

Paradigm

My account