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Image Steganalysis Based on Co-Occurrences of Integer Wavelet Coefficients | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 7، دوره 42، شماره 1، تیر 2010، صفحه 51-59 اصل مقاله (122.88 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2010.107 | ||
| نویسندگان | ||
| M. Abolghasemii* 1؛ H. Aghaeiniaii2؛ K. Faeziii3 | ||
| 1Corresponding Author, M. Abolghasemi is with the Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran (e-mail: mo_abolghasemi@aut.ac.ir). | ||
| 2H. Aghaeinia is with the Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran (e-mail: aghaeini@aut.ac.ir). | ||
| 3K. Faez is with the Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran (e-mail: kfaez@aut.ac.ir). | ||
| چکیده | ||
| We present a steganalysis scheme for LSB matching steganography based on feature vectors extracted from integer wavelet transform (IWT). In integer wavelet decomposition of an image, the coefficients will be integer, so we can calculate co-occurrence matrix of them without rounding the coefficients. Before calculation of co-occurrence matrices, we clip some of the most significant bitplanes of the coefficients. By this preprocessing, in addition to reducing the dimension of feature vector the effects of the embedding are also preserved. We test our algorithm for different embedding rats using Fisher linear discrimination (FLD) classifier and by comparing it with the current state-of-the-art steganalyzers; it is shown that the proposed scheme outperforms them by significant margin. | ||
| کلیدواژهها | ||
| Integer wavelet transform (IWT)؛ LSB matching؛ Steganography؛ Steganalysis | ||
| عنوان مقاله [English] | ||
| Image Steganalysis Based on Co-Occurrences of Integer Wavelet Coefficients | ||
| چکیده [English] | ||
| We present a steganalysis scheme for LSB matching steganography based on feature vectors extracted from integer wavelet transform (IWT). In integer wavelet decomposition of an image, the coefficients will be integer, so we can calculate co-occurrence matrix of them without rounding the coefficients. Before calculation of co-occurrence matrices, we clip some of the most significant bitplanes of the coefficients. By this preprocessing, in addition to reducing the dimension of feature vector the effects of the embedding are also preserved. We test our algorithm for different embedding rats using Fisher linear discrimination (FLD) classifier and by comparing it with the current state-of-the-art steganalyzers; it is shown that the proposed scheme outperforms them by significant margin. | ||
| کلیدواژهها [English] | ||
| Integer wavelet transform (IWT), LSB matching, Steganography, Steganalysis | ||
| مراجع | ||
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[1] Mehdi Kharrazi, Husrev T. Sencar, and Nasir Memon, “Image Steganography: Concepts and Practice” WSPC/Lecture Notes Series, April 22, 2004. [2] Mehdi Kharrazi, Husrev T. Sencar, Nasir Memon, "Performance study of common image steganography and steganalysis techniques" Journal of Electronic Imaging 15(4), 041104 (Oct–Dec 2006). [3] R. Chandramouli, "A mathematical framework for active steganalysis", Multimedia Systems © Springer-Verlag, Vol. 9, pp. 303–311, 2003. [4] J. J. Harmsen, W. A. Pearlman, “Steganalysis of additive noise modelable information hiding” In E. J. Delp and P. W. Wong, editors, Proceedings SPIE, Electronic Imaging, Security, Steganography,and Watermarking of Multimedia Contents V, vol. 5020, pp. 131-142, Santa Clara, CA, January, 2003. [5] A. Ker, "Steganalysis of LSB Matching in Grayscale Images" IEEE Signal Process. Lett., vol. 12, no. 6, pp. 441–444, June, 2005. [6] G. Xuan, Y. Q. Shi, C. Huang, D. Fu, X. Zhu, P. Chai, J. Gao, “Steganalysis Using High-Dimensional Features Derived from Co-occurrence Matrix and Class-Wise Non-Principal Components Analysis (CNPCA)” IWDW, pp. 49-60, 2006 [7] M. Goljan, J. Fridrich, and T. Holotyak, “New blind steganalysis and its implications,” In E. J. Delp and P. W. Wong, editors, Proceedings SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII, vol. 6072, pp. 1-13, January, 2006. [8] J. Zhang, I. J. Cox, and G. Do¨err, “Steganalysis for LSB matching in images with high-frequency noise,” in Proceedings of the IEEE Workshop on Multimedia Signal Processing, October 2007. [9] G. Cancelli, G. Doërr, I. J. Cox, and M. Barni, " Detection Of ±1 LSB Steganography Based On The Amplitude Of Histogram Local Extrema", International Conference on Image Processing (ICIP) 2008. [10] G. Cancelli, G. Doërr, I. J. Cox, and M. Barni, “A comparative study of ±1 steganalyzers,” In Proceedings IEEE, International Workshop on Multimedia Signal Processing, pp. 791-794, Queensland, Australia, October 2008. [11] M. Abolghasemi, H. Aghainia, K. Faez, M. A. Mehrabi, “Detection of LSB±1 steganography based on co-occurrence matrix and bit plane clipping”, SPIE and IS&T. Journal of Electronic Imaging, vol. 19(1), pp. 1-9, March 2010. [12] Haralick, R.M. "Textural features for image classification” IEEE Trans. Systems Man Cybernetics. SMC-3 (1973). [13] T. Fawcett, “ROC graphs: Notes and practical considerations for researchers,” HP Laboratories, Tech. Rep., March 2004. | ||
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