Advertisement

An Adaptive Image Watermarking Scheme Using Non-separable Wavelets and Support Vector Regression

  • Liang Du
  • Xinge You
  • Yiu-ming Cheung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

This paper presents an adaptive image watermarking scheme. Watermark bits are embedded adaptively into the non-separable wavelet domain based on the Human Visual System (HVS) model trained by Support Vector Regression (SVR). Unlike conventional separable wavelet filter banks that limit the ability in capturing directional information, non-separable wavelet filter banks contain the basis elements oriented at a variety of directions and different filter banks are able to capture different detail information. After removing the high frequency components, the low frequency subband used for watermark embedding is more robust against noise and other distortions. In addition, owing to the good generalization ability of the support vector machine, watermark embedding strength can be adjusted according to the HVS value. The superiority of non-separable wavelet transform (DNWT) in capturing image features combined with the good generalization ability of support vector regression provide us with a promising way to design a more robust watermarking algorithm featuring a better trade-off between the robustness and imperceptivity, the main duality of watermarking algorithms. Experimental results show that the DNWT watermarking scheme is robust to noising, JPEG compression, and cropping. In particular, it is more resistant to JPEG compression and noise than the discrete separable wavelet transform based scheme.

Keywords

Digital Non-tensor Product Wavelet Filters Watermarking Human Visual System Support Vector Regression 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lesk, M.: The good, the bad, and the ugly: What might change if we had good DRM. IEEE Security & Privacy 1(3), 63–66 (2003)CrossRefGoogle Scholar
  2. 2.
    Hartung, F., Ramme, F.: Digital right management and watermarking of multimedia content for m-commerce applications. IEEE Communications Magazine 38(11), 78–84 (2000)CrossRefGoogle Scholar
  3. 3.
    Yu, P.T., Tsai, H.H., Lin, J.S.: Digital watermarking based on neural networks for color images. Signal Processing 81, 663–671 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Wang, Y.W., Doherty, J.F., Van Dyck, R.E.: A wavelet-based watermarking algoritm for ownership verification of digital images. IEEE Transactions on Image Processing 11, 77–87 (2002)CrossRefGoogle Scholar
  5. 5.
    Cox, I.J., Miller, M.L.: The first 50 years of electronic watermarking. Journal on Applied Signal Processing 2, 126–132 (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kovac̆ević, J., Vetterli, M.: Non-separable multidimensional perfect reconstruction filter banks and wavelet bases for R n. IEEE Transactions on Information Theory 38(2), 533–555 (1992)Google Scholar
  7. 7.
    You, X.G., Zhang, D., Chen, Q.H.: Face representation by using non-tensor product wavelets. In: Proceedings of International Conference on Pattern Recognition, pp. 503–506 (2006)Google Scholar
  8. 8.
    Watson, A.B.: DCT quantization matrices optimized for individual images. In: Human Vision, Visual Processing,and Digital Display IV, Proceedings SPIE 1913-1914, pp. 202–216 (1993)Google Scholar
  9. 9.
    Watson, A.B.: Visually optimal DCT quantization matrices for individual images. In: Proceedings of IEEE Data Compression Conference, pp. 178–187 (1993)Google Scholar
  10. 10.
    Lou, D.C., Liu, J.L., Hu, M.C.: Adaptive digital watermarking using neural network technique. In: Proceedings of IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, vol. 37(10), pp. 325–332 (2003)Google Scholar
  11. 11.
    Yu, P.T., Tsai, H.H., Lin, J.S.: Digital watermarking based on neural networks for color images. Signal Processing 81, 663–671 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Zhang, J., Wang, N.C.: Neutral network based watermarking for image authentication. Journal of Computer-Aided Design & Computer Graphics 15(3), 307–312 (2003)Google Scholar
  13. 13.
    Davis, K.J., Najarian, K.: Maximizing stength of digital watermarks using neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 2893–2898 (2001)Google Scholar
  14. 14.
    Li, C.H., Lu, Z.D., Zhou, K.: An image watermarking technique based on support vector regression. In: Proceedings of ISCIT 2005, pp. 177–180 (2005)Google Scholar
  15. 15.
    Li, J., Peng, H., Pei, Z.: Adaptive watermarking algorithm using SVR in wavelet domian. In: 6th IEEE/ACIS International Conference on Computer and Information Science, pp. 207–211 (2007)Google Scholar
  16. 16.
    Huang, J.W., Yao, R.H.: Adaptive image watermarking algorithm. Jounal of Image and Graphics 4, 640–643 (1999)Google Scholar
  17. 17.
    Yi, K.X., Shi, J.Y.: Adaptive 2-dimension image watermarking algorithm. Jounal of Image and Graphics 6, 444–449 (2001)Google Scholar
  18. 18.
    Chen, B., Wornel, G.: An information-theoretic approach to design of robust digital watermarking systems. In: Proceedings of IEEE International Conference on Acoustics, Speech and Singnal Processing, vol. 4, pp. 2061–2064 (1999)Google Scholar
  19. 19.
    Petitcolas, F.A.P.: Weakness of Existing Watermark Scheme (1997), http://www.petitcolas.net/fabien/watermarking/stirmark/index.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Liang Du
    • 1
  • Xinge You
    • 1
  • Yiu-ming Cheung
    • 2
  1. 1.Department of Electronics and Information EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of Computer ScienceHong Kong Baptist UniversityHong Kong SARChina

Personalised recommendations