The Analysis of Steganographic by Sub-Pixel Calibration

Abstract:

In order to achieve high accuracy, we present a new calibration technique aimed at blind steganalysis. The calibration can be considered as a preprocessing of stego image before extracting the features. The extracted features will be more effective for our classification [1] Moreover, the calibrated feature was used to train SVM (Support Vector Machine)[2], a nonlinear classifier, which is effective in class separation. For comparison, we conducted extensive experiments and drawn a conclusion that the steganalytic scheme based on our novel calibration can detect the stego information with high accuracy. Experimental results demonstrate that our proposed scheme outperforms the best effective JPEG steganalysis having been presented.

• Periodical: Applied Mechanics and Materials (Volumes 599-601)
• Pages: 1753-1756
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.599-601.1753
• Online since: August 2014
• Authors: Qian Chen*, Jing Xin Hong, Gang Ou, Xu Chen, Ling Wang, Yi Xiong Zhang
• Keywords: Calibration, Steganalysis, Sub-Pixel, Translation

References

[1] S. S. Keerthi, V. Sindhwani, and O. Chapelle. An efficient method for gradient-based adaptation of hyperparameters in svm models. In B. Scholkopf, J. Platt, and T. Homan, editors, Advances in Neural Information Processing Systems 19, pages 673{680. MIT Press, Cambridge, MA, (2007).

DOI: 10.7551/mitpress/7503.003.0089

[2] Chang C -C, Lin C -J. LIBSVM: a library for support vector machines, Software available at http: /www. csie. ntu. edu. tw/cjlin/libsvm, (2001).

[3] Luo X Y, Liu F L, Chen J M, and et al., Image universal steganalysis based on wavelet packet transform, in Proc. 10th International Workshop on Multimedia Signal Processing, pp.780-784, (2008).

DOI: 10.1109/mmsp.2008.4665180

[4] R. Chandramouli, M. Kharrazi, and N. Memon, Image steganography and steganalysis concepts and practice, Proc. of IWDW'03, vol. 2939, pp.35-49, Springer, (2003).

DOI: 10.1007/978-3-540-24624-4_3

[5] Pevny T, Fridrich J. Merging Markov and DCT features for multi-class JPEG steganalysis, in Electronic Imaging. International Society for Optics and Photonics. p.650503. (1), (2007).

DOI: 10.1117/12.696774

[6] Farid H, Siwei L. Detecting hidden messages using higher-order statistics and support vector machines. In: Information Hiding, 5th International Workshop, volume 2578 of Lecture Notes in Computer Science,. 340–354, (2002).

DOI: 10.1007/3-540-36415-3_22

[7] Y. Wang and P. Moulin, Optimized feature extraction for learning based image steganalysis, IEEE Trans. Inf. Forensics Secur, vol. 2, no. 1, pp . 31-45, (2007).

DOI: 10.1109/tifs.2006.890517

[8] T. Pevny, J. Fridrich: Multiclass Blind Steganalysis for JPEG Images , Proc. SPIE Electronic Imaging, Photonics West, January (2006).

[9] Shimizu, M.; Okutomi, M, Significance and attributes of subpixel estimation on area-based matching.

[10] Niels Provos and Peter Honeyman, Hide and seek: An introduction to steganography, IEEE Security and Privacy, vol. 1, no. 3, pp.32-44, (2003).

DOI: 10.1109/msecp.2003.1203220