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Secured Data Hiding based on Compression Function and Quantization

Ajit Danti, G.R.Manjula Published in Information Systems

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
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  1. Ajit Danti and G.R.Manjula. Article: Secured Data Hiding based on Compression Function and Quantization. International Journal of Applied Information Systems 1(2):53-58, January 2012. BibTeX

    	author = "Ajit Danti and G.R.Manjula",
    	title = "Article: Secured Data Hiding based on Compression Function and Quantization",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 1,
    	number = 2,
    	pages = "53-58",
    	month = "January",
    	note = "Published by Foundation of Computer Science, New York, USA"


Data hiding is the process of secretly embedding information inside a data source without changing its perceptual quality. In this paper, Quantization Index Modulation and the compression function of µ-Law standards for quantization are used. The proposed method transforms the host signal into the logarithmic domain using the µ-Law compression function. Then, the transformed data is quantized uniformly and the result is transformed back to the original domain using the inverse function. The scalar and the vector methods along with a secret key for data hiding will make the method more secure and efficient. The experimental results demonstrate the robustness of the proposed approach.


  1. Chen and G. W. Wornell, “Quantization index modulation methods: A class of provably good methods for digital watermarking and information embedding,” IEEE Trans. Inf. Theory, vol. 47, no. 4, pp. 1423–1443, May 2001.
  2. A.M.Kondaz”Digital Speech: Coding for Low Bit Rate Communication System”, 2nd edition Wiley Publishers.
  3. Rafael C Gonzalez and Richard E Woods, Digital Image Processing. 3rd Edition Pearson Education Hall.
  4. N.K.Kalantari, Seyed Mohammad Ahadi “A Logarithmic Quantization Index Modulation for Perceptually Better Data Hiding” IEEE Trans on Image Processing, Vol 19, no 6, June 2010.
  5. P.Comesana and F.Perez Gonzalez “On a watermarking scheme in the logarithmic domain and its perceptual advantages”, presented at the IEEE Int. Conf. Image Processing, San Antonio, TX, Sep. 2007
  6. M.Barti, F.Bartolini, A DE Rosa and A.Piva, ”A new decoder for the optimum recovery of non-additive watermarks,” IEEE Trans. Image Process., vol. 6, no. 12, pp. 1673-1687, Dec. 1997.
  7. I.J.Coax and Jean Paul , ”Some general methods for tampering with watermarks”, IEEE J.Sel.Areas Commun, vol 16, no4, pp587-593, May 1998.
  8. Jonathan Pinel, Laurent Girin, Cléo Baras and Mathieu Parvaix “A high-capacity watermarking technique for audio signals based on MDCT-domain quantization” Proceedings of 20th International Congress on Acoustics, ICA, 23–27 August 2010
  9. A.U. Yarg?c “Hidden data transmission in mixed excitation linear prediction coded speech using quantization index modulation” Published in IET Information Security, Received on 3rd June 2009, Revised on 3rd May 2010
  10. B. Chen, Design and Analysis of Digital Watermarking, Information Embedding and Data hiding systems. PhD Thesis, MIT Cambridge, MA, June 2000.
  11. J.Chou, K.Ramachandran and A.Ortega, “Next generation techniques for robust and imperceptible audio data hiding,” Proc IEEE Int. Conf. Acoustics, Speech. Signal processing (ICASSP), May 2001, vol. 3, pp. 1349-1352.
  12. I. J. Cox, J. Killian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Trans. Image Processing, vol. 6, pp. 1673–1687, Dec. 1997.
  13. Zoran Peric, Aleksandar Mosic And Stefan Panic “Coding Algorithm Based on Loss Compression using Scalar Quantization Switching Technique and Logarithmic Companding” Journal Of Information Science And Engineering 26, 967-976, 2010
  14. T. Villmann and S. Haase “Mathematical Aspects of Divergence Based Vector Quantization Using Frechet-Derivatives” Machine Learning Reports, Research group on Computational Intelligence,, 2010
  15. William Staling ”Cryptography and Network Security” 2nd edition Pearson Education Hall.


Quantization, Data hiding, Information security, Digital water marking