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An Image Steganography Algorithm against Histogram and Statistical Analysis

Dr. V. Vijayalakshmi, Dr. G. Zayaraz, V. Nagaraj


Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover images. The Least Significant Bit (LSB) steganography that replaces the least significant bits of the host medium is a widely used technique with low computational complexity and high insertion capacity. Although it has good perceptual transparency, it is vulnerable to steganalysis which is based on statistical analysis. Many other steganography algorithms have been developed such as spread spectrum embedding, Discrete Cosine Transforms (DCT) and Discrete Wavelet Transform (DWT). But in all these existing schemes the hidden secret message in a cover message can be easily detected from the histogram analysis and statistical analysis. Therefore developing new LSB steganography algorithms against histogram and statistical analysis is a critical issue. This paper proposes a modulo based image steganography algorithm for both colour as well as for black and white images. Histogram and statistical analysis performed on the stego image proved that the proposed method can effectively resist statistical steganalysis.


Least Significant Bit (LSB), Embedding, Stego image, Histogram analysis, Statistical analysis.

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A. Ker, “Batch steganography and pooled steganalysis,” Magazine of IEEE Multimedia Special Issue on Security, pp. 22-28, Oct. 2003.

A. Ker, “A general framework for the structural steganalysis of LSB replacement,” proceedings of 7th Information Hiding Workshop, vol. 3727, Springer LNCS, pp. 296–311, Sep. 2005

R. Chandramouli, M. Kharrazi, and N. Memon, “Image steganography and steganalysis: Concepts and practice,” proceedings of Int.Workshop Digital Watermarking 2003, 2004, vol. 2939, pp. 35–49.

J. L. Jun Cheng, Alex C. Kot and H. Cao, “Steganalysis of Data Hiding in binary Text Messages,” in ISCAS, pp. 4405–4408, May 2005.

U. M. S. Onkar Dabeer, kenneth Sullivan and B. S. Manjunath, “Detection of Hiding in the Least Significant Bit,” in IEEE Transaction on Signal Processing, pp. 362–375, Oct 2004.

L. Zhi and S. A. Fen, “Detection of Random LSB Image Steganography,”in International Conference on Information Technology, pp. 2113–2117, April 2004.

H. Zhang and H. Tang, “A Novel image steganography algorithm against statistical analysis,” proceedings of the IEEE, vol. 19, no. 22, pp. 3884-3888, Aug. 2007.

J. Fridrich, M. Goljan, and R. Du, “Detecting LSB Steganography in Color and Gray-Scale Images,” Magazine of IEEE Multimedia Special Issue on Security, pp. 22-28, Nov. 2001.

F. Petioles, J. Anderson, and G. Kuhn, “Information hiding-A survey,” proceedings of the IEEE, vol. 87, no. 7, pp.1062-1078, June 1999.

Gougelet Pierre-emmanuel.: XnView, Software available at http://

C.Y. Yang, “Color Image Steganography based on Module Substitutions,” The 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kaouhsiung, Taiwan, Nov. 2007.

J. Kang, Y. You, and M. Young Sung, “Steganography using Block-based Adaptive Threshold,” Proceeding of the IEEE, vol. 11, pp.234-241 Nov. 2007.

M .T. Parvez and A. Aziz Gutub, “RGB Intensity Based Variable-Bits Image Steganography,” Proceedings of IEEE Asia-Pacific Services Computing Conference, Yilan, Taiwan, Dec.2008.

J. Fridrich, M. Goljan, and R. Du, “Practical Steganalysis of Digital Images- State of the Art,” Proceedings of SPIE, vol. 4675, pp. 1-13, Jan. 2002.

H. Farid, “ Detecting Hidden Messages using Higher Order Statistical Models,” Proceedings of the IEEE, vol. 15, no. 6, pp. 68-72, Oct. 2002.


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