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Shahanaghi A, Ali Akhaee M A, Sarreshtedari S, Toosi R. Optimum Group Pixel Matching Strategies for Image Steganography. International Journal of Information and Communication Technology Research 2021; 13 (4) :43-52
URL: http://ijict.itrc.ac.ir/article-1-498-en.html
1- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran , a.shahanaghi@ut.ac.ir
2- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract:   (1526 Views)

LSB matching techniques are widely applied in the field of image steganography. In such algorithms, pixel values of each group must be changed in a way that a predefined function of the pixel group matches the secret digit. The notational system of the secret digits can be every desired number, as well as the size of the pixel groups. In order to preserve the quality of the stego image, it is desired to limit the changes in the pixel groups as much as possible. Therefore, optimum strategies must be found to match the function of the pixel group to the secret digit with the least possible imposed distortion in terms of mean square error. Having been recently found for pixel pairs, such strategies are found for the larger pixel groups by the proposed method in this paper. Among all the strategies providing the similar minimum MSE value, the one is chosen that helps to preserve the histogram of the original image. Optimum strategies found for all notational systems and pixel group sizes makes the algorithm flexible for various application with different payloads, while it improves the similar techniques in terms of both MSE reduction and histogram preservation, as is confirmed by the experimental results.

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References
1. [1] M. A. Akhaee and F. Marvasti, “A survey on digital data hiding schemes: Principals, algorithms, and applications.” ISeCure, vol. 5, no. 1, 2013. [2] R. Toosi, M. Sadeghi, and M. A. Akhaee, “Robust image watermarking using sample area quantization,” Multimedia Tools and Applications, vol. 78, no. 24, pp. 34 963–34 980, 2019. [3] A. Westfeld and A. Pfitzmann, “Attacks on steganographic systems,” in Proc. 3rd Int. Workshop on Information Hiding, vol. 1768, 1999, pp. 61–76. [4] J. Harmsen and W. A. Pearlman, “Steganalysis of additive-noise modelable information hiding,” in Society of Photo-Optical Instrumentation Engineers (SPIE) Conf., vol. 5020, Jun. 2003, pp. 131–142. [5] A. D. Ker, “Steganalysis of LSB matching in grayscale images,” IEEE Signal Process. Lett., vol. 12, no. 6, pp. 441 – 444, Jun. 2005. [6] Y. Q. Shi, C. Chen, and W. Chen, “A markov process based approach to effective attacking JPEG steganography,” in Information Hiding. Springer, 2007, pp. 249–264. [7] J. Kodovsky and J. Fridrich, “Steganalysis in high dimensions: Fusing ` classifiers built on random subspaces,” in IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2011, pp. 78 800L– 78 800L. [8] Pevny, P. Bas, and J. Fridrich, “Steganalysis by subtractive pixel adjacency matrix,” Information Forensics and Security, IEEE Transactions on, vol. 5, no. 2, pp. 215–224, 2010. [9] J. Kodovsky, J. Fridrich, and V. Holub, “Ensemble classifiers for steganalysis of digital media,” Information Forensics and Security, IEEE Transactions on, vol. 7, no. 2, pp. 432–444, 2012. [10] R. Toosi, S. Salehkalaibar, and M. A. Akhaee, “Improved ensemble growing method for steganalysis of digital media,” Multimedia Tools and Applications, vol. 78, no. 8, pp. 9877–9893, 2019. [11] M. Hamghalam, S. Mirzakuchaki, and M. A. Akhaee, “Robust image watermarking using dihedral angle based on maximum-likelihood detector,” IET Image Processing, vol. 7, no. 5, pp. 451–463, 2013. [12] M. Sadeghi, R. Toosi, and M. A. Akhaee, “Blind gain invariant image watermarking using random projection approach,” Signal Processing, vol. 163, pp. 213–224, 2019. [13] J. Mielikainen, “LSB matching revisited,” Signal Processing Letters, IEEE, vol. 13, no. 5, pp. 285–287, 2006. [14] . Sarreshtedari, M. Ghotbi, and S. Ghaemmaghami, “One-third probability embedding: Less detectable LSB steganography,” in Multimedia and Expo, ICME 2009. IEEE International Conference on, 2009, pp. 1002–1005. [15] X. Li, B. Yang, D. Cheng, and T. Zeng, “A generalization of LSB matching,” Signal Processing Letters, IEEE, vol. 16, no. 2, pp. 69 – 72, feb. 2009. [16] M. A. Akhaee, S. M. E. Sahraeian, and F. Marvasti, “Contourlet-based image watermarking using optimum detector in a noisy environment,” IEEE Transactions on Image Processing, vol. 19, no. 4, pp. 967–980, 2009. [17] H. M. Sun, K. H. Wang, C. C. Liang, and Y. S. Kao, “A LSB substitution compatible steganography,” in TENCON 2007 - IEEE Region 10 Conf., Nov. 2007, pp. 1 –3. [18] S. Sarreshtedari and M. Akhaee, “One-third probability embedding: a new ± one histogram compensating image least significant bit steganography scheme,” Image Processing, IET, vol. 8, no. 2, pp. 78–89, February 2014. [19] D. C. Wu and W. H. Tsai, “A steganographic method for images by pixel-value differencing,” Pattern Recognition Letters, vol. 24, no. 9–10, pp. 1613 – 1626, 2003. [20] W. Luo, F. Huang, and J. Huang, “Edge adaptive image steganography based on LSB matching revisited,” Info. Forensics and Security, IEEE Trans. on, vol. 5, no. 2, pp. 201 –214, Jun. 2010. [21] H. M. Sun, C. Y. Weng, C. F. Lee, and C. H. Yang, “Anti-forensics with steganographic data embedding in digital images,” Selected Areas in Communications, IEEE Journal on, vol. 29, no. 7, pp. 1392 –1403, Aug. 2011. [22] . Zhang and S. Wang, “Efficient steganographic embedding by exploiting modification direction,” Communications Lett., IEEE, vol. 10, no. 11, pp. 781 –783, Nov. 2006. [23] R. M. Chao, H. C. Wu, C. C. Lee, and Y. P. Chu, “A novel image data hiding scheme with diamond encoding,” EURASIP J. Inf. Security, vol. 2009, 2009. [24] H.-S. Leng, J.-F. Lee, and H.-W. Tseng, “A high payload emd-based steganographic method using two extraction functions,” Digital Signal Processing, vol. 113, p. 103026, 2021. [25] S. Saha, A. Chakraborty, A. Chatterjee, S. Dhargupta, S. K. Ghosal, and R. Sarkar, “Extended exploiting modification direction based steganography using hashed-weightage array,” Multimedia Tools and Applications, vol. 79, no. 29, pp. 20 973–20 993, 2020. [26] Q. Ke, Q. Liao, and R. Pan, “An improved emd parallel steganography algorithm,” in Journal of Physics: Conference Series, vol. 1621, no. 1. IOP Publishing, 2020, p. 012006. [27] R. Atta, M. Ghanbari, and I. Elnahry, “Advanced image steganography based on exploiting modification direction and neutrosophic set,” Multimedia Tools and Applications, pp. 1–19, 2021. [28] W. Hong and T. S. Chen, “A novel data embedding method using adaptive pixel pair matching,” Info. Forensics and Security, IEEE Trans. on, vol. 7, no. 1, pp. 176 –184, Feb. 2012. [29] J. Fridrich and J. Kodovsky, “Rich models for steganalysis of digital images,” Information Forensics and Security, IEEE Transactions on, vol. 7, no. 3, pp. 868–882, June 2012. [30] The Dataset from the 2nd Bows Contest. (2012, Mar. 26) [Online]. Available: http://bows2.ec-lille.fr/.

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