DOI: 10.17586/2226-1494-2018-18-1-113-121


ACCURACY INCREASE FOR STEGANALYSIS METHODS BY OPTIMIZATION OF WAVELET TRANSFORM PARAMETERS

A. V. Sivachev, N. N. Prokhozhev, O. V. Mikhailichenko


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For citation: Sivachev A.V., Prokhozhev N.N., Mikhailichenko O.V. Accuracy increase for steganalysis methods by optimization of wavelet transform parameters. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 1, pp. 113–121 (in Russian). doi: 10.17586/2226-1494-2018-18-1-113-121

Abstract

 Subject of Research. We have performed the study for accuracy increase of steganalysis methods used for detection of information hidden in wavelet domain. Methods. The methods proposed by Gireesh Kumar, Hany Farid, Changxin Liu, Yun Q. Shi demonstrate low efficiency when detecting the fact of embedding into the discrete wavelet transform domain. The paper considers the reasons for this fact. The basis of these methods is application of statistical moments for LL, HL, LH, HH domains obtained by wavelet transformation and additional image parameters making up a support vector. Main Results. We have analyzed the reasons for low efficiency of modern steganalysis methods in tasks of embedding fact detection in LH and HL domains that represents about 65% and 70% of correctly qualified images, respectively. The analyzed methods of steganalysis use such parameters as statistical moments, which have a high degree of variability that makes it difficult to classify images uniquely. We propose the technique for steganalysis accuracy increase by evaluation and taking account of the parameters’ error used by steganalysis methods by means of the specialized wavelet. The proposed technique gives the possibility to increase classification accuracy of images by 2.5–7.0 %. Practical Relevance. The results of work are useful for specialists in the field of information security in tasks of detection and countermeasures for the hidden transmission channels. The obtained results can be useful for development of steganalysis systems.


Keywords: steganography, stego image, passive resistance, hidden transmission channel, steganalysis system and methods, binary classification, discrete wavelet transform, two-dimensional wavelet transform, Haar wavelet

References
1.        Evsutin O.O., Negacheva E.V.Steganographic embedding of information into digital images compressed with the use of block cellular automata. Proceedings of TUSUR, 2013, no. 4, pp. 130–135.(In Russian)
2.        Gayathri C., Kalpana V. Study on image steganography techniques. International Journal of Engineering and Technology, 2013, vol. 5, no. 2, pp. 572–577.
3.        SPYCRAFT: The Secret History of the CIA’s Spytechs from Communism to Al-Qaeda. Available at: https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/csi-studies/studies/vol52no2/spycraft-the-secret-history-of-the-cia2019s.html (accessed 01.11.2017)
4.        Nissar A., Mir A.H. Classification of steganalysis techniques: a study. Digital Signal Processing: A Review Journal, 2010, vol. 20, no. 6, pp. 1758–1770. doi: 10.1016/j.dsp.2010.02.003
5.        GireeshKumarT., JithinR., Shankar D.D. Featurebasedsteganalysis using wavelet decomposition and magnitude statistics. Proc. Int. Conf. on Advances in Computer Engineering. Bangalore, India, 2010, pp. 298–300. doi: 10.1109/ACE.2010.33
6.        Hany F. Detecting Steganographic Messages in Digital Images. Technical Report TR2001-412, Dartmouth College, 2001.
7.        Liu C., Ouyang C., Guo M., Chen H. Image steganalysis based on spatial domain and DWT domain features.Proc. 2nd int. Conf. on Networks Security, Wireless Communications and Trusted Computing, 2010,vol. 1, pp. 329–331. doi: 10.1109/NSWCTC.2010.271
8.        Shi Y.Q., XuanG., Yang C., Gao J., Zhang Z., Chai P., Zou D., Chen C., Chen W. Effective steganalysis based on statistical moments of wavelet characteristic function. Proc. Int. Conf. on Information Technology: Coding and Computing, ITCC'05. Las Vegas, USA, 2005, vol. 2, pp. 768–773.
9.        Schaathun H.G.Machine Learning in Image Steganalysis. Wiley-IEEE Press, 2012, 290 p.
10.      Prokhozhev N.N., Mikhailichenko O.V., Bashmakov D.A., Sivachev A.V., Korobeinikov A.G. Software Study the effectiveness of statistical algorithms of quantitative steganalysis in the task of detecting hidden information channels. Systems and Computational Methods, 2015, no. 3, pp. 281–292. (In Russian) doi: 10.7256/2305-6061.2015.3.17233
11.      Prokhozhev N., Mikhailichenko O., Sivachev A., Bashmakov D., Korobeynikov A.G. Passive steganalysis evaluation: reliabilities of modern quantitative steganalysis algorithms. Advances in Intelligent Systems and Computing, 2016,vol. 451, pp. 89–94. doi:10.1007/978-3-319-33816-3_9
12.      Sivachev A.М., Prokhozhev N.N., Mikhailichenko O.V., Bashmakov D.A. Effectiveness of steganalysis based on machine learning methods. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 3, pp. 457–466 (In Russian). doi: 10.17586/2226-1494-2017-17-3-457-466
13.      Smirnov V.I. A Course of Higher Mathematics. St. Petersburg, BKhV-Peterburg Publ., 2008, vol. 1, 614 p.
14.      BOWS2 the 10000 original images. Available at: http://bows2.ec-lille.fr/ (accessed 17.07.2016).
15.      WaliaR. Steganography based on neighborhood pixels. Proc. 2nd Conf. on Advances in Computing, Communications and Informatics, ICACCI. Mysore, India,2013, pp. 203–206. doi: 10.1109/ICACCI.2013.6637171
16.      Qin J., Xiang X., Deng Y., Li Y., Pan L. Steganalysis of highly undetectable steganography using convolution filtering. Information Technology Journal, 2014, vol. 13, no. 16, pp. 2588–2592.doi: 10.3923/itj.2014.2588.2592


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