Digital Watermarking Based on Magic Square and Ridgelet Transform Techniques

  • Rama Seshagiri Rao Channapragada
  • Munaga V. N. K. Prasad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)

Abstract

This paper proposes two algorithms for embedding and extraction of the watermark into the cover image based on magic square and ridgelet transform techniques. Spread-spectrum communication systems use the spread sequences that have good correlation properties. Magic square technique is used as a spread-spectrum technique to spread the watermark. Ridgelet transform is the next-generation wavelets as it is effective through line singularities characteristic. Ridgelet transform generates sparse image representation where the most significant coefficient represents the most energetic direction of an image with straight edges. The experiments indicated that these algorithms enabled the cover images to have the good invisibility and made them robust to the general image compression attacks such as JPEG, GIF.

Keywords

Digital watermarking Magic square Ridgelet transformation Peak signal-to-noise ratio Wavelet transformation 

References

  1. 1.
    Bae, Y., Lee, H.: A sentiment analysis of audiences on twitter: Who is the positive or negative audience of popular twitterers? 5th International Conference on Convergence and Hybrid Information Technology (ICHIT 2011), pp. 732–739, Sept 2011Google Scholar
  2. 2.
  3. 3.
    Iannella, R.: Digital rights management (DRM) architectures. D-Lib Magazine 7(6) (2001)Google Scholar
  4. 4.
    Cox, I.J., Miller, M.L., Bloom, J.A.: Digital watermarking, pp. 12–26. Academic Press, Waltham (2002)Google Scholar
  5. 5.
    Hunter, D.: Handmade paper and its watermarks: A bibliography. B. Franklin, New York (1967)Google Scholar
  6. 6.
    Meggs, P.B.: A history of graphic design, 3rd edn, p. 58. Wiley, New Jersey (1998)Google Scholar
  7. 7.
    Pickholtz, R.L., Schilling, D.L., Milstein, L.B.: Theory of spread spectrum communications: A tutorial. IEEE Trans. Commun. (COM-30), 30(5), 855–884 (1982)Google Scholar
  8. 8.
    Chang, C.C., Kieu, D., Wang, Z.H., Li, M.C.: An image authentication scheme using magic square. 2nd IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 1–4, Aug 2009Google Scholar
  9. 9.
    Huang, H.: Perceptual image watermarking algorithm based on magic squares scrambling in DWT. 5th International Joint conference on INC, IMS, IDC, pp. 1819–1822, Aug 2009Google Scholar
  10. 10.
    Li, Y.: An image digital watermarking method based on ridgelet and KICA. International Conference on MultiMedia and Information Technology (MMIT), pp. 345–348, Dec 2008Google Scholar
  11. 11.
    Campisi, P., Kundur, D., Neri, A.: Robust digital watermarking in the ridgelet domain. IEEE Signal Process. Lett. 11(10), 826–830 (2004)Google Scholar
  12. 12.
    Pradhan, C., Rath, S., Bisoi, A.K.: Non blind digital watermarking technique using DWT and cross chaos. 2nd International Conference on Communication, Computing and Security (ICCCS-2012), vol. 6, pp. 897–904 (2012)Google Scholar
  13. 13.
    Bhatnagar, G., Jonathan Wu, Q.M., Raman, B.: Discrete fractional wavelet transform and its application to multiple encryption. Int. J. Inf. Sci. 223, 297–316 (2013)Google Scholar
  14. 14.
    Zhang, C., Hu, M.: Curvelet image watermarking using genetic algorithms. Congr. Image Signal Process. 1, 486–490 (2008)Google Scholar
  15. 15.
    Niu, P.P., Wang, X.Y., Jin, H.B., Lu, M.Y.: A feature-based robust digital image watermarking scheme using bandelet transform. Int. J. Opt. Laser Technol. 43(3), 437–450 (2011)CrossRefGoogle Scholar
  16. 16.
    Sadreazami, H., Amini, M.: A robust spread spectrum based image watermarking in ridgelet domain. AEU: Int. J. Electron. Commun. 66(5), 364–371 (2012)Google Scholar
  17. 17.
    Wang, J., Peng, H., Shi, P.: An optimal image watermarking approach based on a multi-objective genetic algorithm. Int. J. Inf. Sci. 181(24), 5501–5514 (2011)Google Scholar
  18. 18.
    Xie, T.: An evolutionary algorithm for magic squares. Congr. Evol. Comput. 2, 906–913 (2003)Google Scholar
  19. 19.
    Andrews, W.S., Frierson, L.S., Browne, C.A.: Magic squares and cubes. Open court publish company (1908)Google Scholar
  20. 20.
    Cox, J., Kilian, J., Leighton, T., Shamoon, T.: Secure spread spectrum watermarking for images, audio and video. Int Conf. Image Process. 3, 243–246 (1996)Google Scholar
  21. 21.
    Cox, J., Kilian, J., Leighton, T., Shamoon, T.: Secure spread spectrum watermarking for multimedia. IEEE Trans. Image Process. 6(12), 1673–1687 (1997)CrossRefGoogle Scholar
  22. 22.
    Starck, J.L., Candès, E.J., Donoho, D.L.: The curvelet transform for image denoising. IEEE Trans. Image Process. 11(6), 670–684 (2002)Google Scholar
  23. 23.
    Donoho, D.L., Flesia, A.G.: Digital ridgelet transform based on true ridge functions. Int. J. Stud. Comput. Math. 10, 1–30 (2003)Google Scholar
  24. 24.
    Do, M.N., Vetterli, M.: The finite ridgelet transform for image representation. IEEE Trans. Image Process. 12(1), 16–28 (2003)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Rama Seshagiri Rao Channapragada
    • 1
  • Munaga V. N. K. Prasad
    • 2
  1. 1.Department of CSEGeethanjali College of Engineering and TechnologyHyderabadIndia
  2. 2.IDRBTHyderabadIndia

Personalised recommendations