An efficient singular value decomposition algorithm for digital audio watermarking



The singular value decomposition (SVD) mathematical technique is utilized, in this paper, for audio watermarking in time and transform domains. Firstly, the audio signal in time or an appropriate transform domain is transformed to a 2-D format. The SVD algorithm is applied on this 2-D matrix, and an image watermark is added to the matrix of singular values (SVs) with a small weight, to guarantee the possible extraction of the watermark without introducing harmful distortions to the audio signal. The transformation of the audio signal between the 1-D and 2-D formats is performed in the well-known lexicographic ordering method used in image processing. A comparison study is presented in the paper between the time and transform domains as possible hosting media for watermark embedding. Experimental results are in favor of watermark embedding in the time domain if the distortion level in the audio signal is to be kept as low as possible with a high detection probability. The proposed algorithm is utilized also for embedding chaotic encrypted watermarks to increase the level of security. Experimental results show that watermarks embedded with the proposed algorithm can survive several attacks. A segment-by-segment implementation of the proposed SVD audio watermarking algorithm is also presented to enhance the detectability of the watermark in the presence of severe attacks.

Audio watermarking SVD DFT DST DCT DWT 


  1. Chu, W. C. (2003). DCT-based image watermarking using subsampling. IEEE Transactions on Multimedia, 5(1), 34–38. CrossRefGoogle Scholar
  2. Crochiere, R. E., Tribolet, J. E., & Rabiner, L. R. (1980). An interpretation of the log likelihood ratio as a measure of waveform coder performance. IEEE Transactions on Acoustics, Speech, And Signal Processing, 28(3), 318–323. CrossRefGoogle Scholar
  3. Erküçük, S., Krishnan, S., & Glu, M. Z. (2006). A robust audio watermark representation based on linear chirps. IEEE Transactions on Multimedia, 8(5), 925–936. CrossRefGoogle Scholar
  4. Fridrich, J. (1997). Image encryption based on chaotic maps. In Proceedings of the IEEE international conference on systems, man, and cybernetics (pp. 1105–1110). Google Scholar
  5. Ghouti, L., Bouridane, A., Ibrahim, M. K., & Boussakta, S. (2006). Digital image watermarking using balanced multiwavelets. IEEE Transactions on Signal Processing, 54(4), 1519–1536. CrossRefGoogle Scholar
  6. Guillemain, P., & Martinet, R. K. (1996). Characterization of acoustic signals through continuous linear time-frequency representations. Proceedings of the IEEE, 84(4), 561–585. CrossRefGoogle Scholar
  7. Han, F., Yu, X., & Han, S. (2006). Improved Baker map for image encryption. In Proceedings of the first international symposium on systems and control in aerospace and astronautics, ISSCAA 2006 (pp. 1273–1276). Google Scholar
  8. Huang, F., & Lei, F. (2008). A novel symmetric image encryption approach based on a new invertible two-dimensional map. In Proceedings of the international conference on intelligent information hiding and multimedia signal processing IIHMSP 2008 (pp. 1340–1343). Google Scholar
  9. Kim, H. S., & Lee, H. K. (2003). Invariant image watermark using Zernike moments. IEEE Transactions on Circuits and Systems for Video Technology, 13(8), 766–775. CrossRefGoogle Scholar
  10. Koduru, S. C., & Chandrasekaran, V. (2008). Integrated confusion-diffusion mechanisms for chaos based image encryption. In IEEE 8th international conference on computer and information technology workshops (pp. 260–263). Google Scholar
  11. Kubichek, R. (1993). Mel-cepstral distance measure for objective speech quality assessment. In Proceedings of the IEEE pacific rim conference on communications, computers and signal processing (pp. 125–128). Google Scholar
  12. Lemma, A. N., Aprea, J., Oomen, W., & de Kerkhof, L. V. (2003). A temporal domain audio watermarking technique. IEEE Transactions on Signal Processing, 51(4), 1088–1097. CrossRefMathSciNetGoogle Scholar
  13. Li, W., Xue, X., & Lu, P. (2006). Localized audio watermarking technique robust against time-scale modification. IEEE Transactions on Multimedia, 8(1), 60–69. CrossRefGoogle Scholar
  14. Lim, J. S. (1990). Two-dimensional signal and image processing. New York: Prentice Hall. Google Scholar
  15. Liu, Z., & Inoue, A. (2003). Audio watermarking techniques using sinusoidal patterns based on pseudorandom sequences. IEEE Transactions on Circuits and Systems for Video Technology, 13(8), 801–812. CrossRefGoogle Scholar
  16. Liu, R., & Tan, T. (2002). An SVD-based watermarking scheme for protecting rightful ownership. IEEE Transactions on Multimedia, 4(1), 121–128. CrossRefGoogle Scholar
  17. Lu, Z. M., Xu, D. G., & Sun, S. H. (2005). Multipurpose image watermarking algorithm based on multistage vector quantization. IEEE Transactions on Image Processing, 14(6), 822–831. CrossRefGoogle Scholar
  18. Macq, B., Dittmann, J., & Delp, E. J. (2004). Benchmarking of image watermarking algorithms for digital rights management. Proceedings of the IEEE, 92(6), 971–984. CrossRefGoogle Scholar
  19. McDermott, B. J., Scaglia, C., & Goodman, D. J. (1978). Perceptual and objective evaluation of speech processed by adaptive differential PCM. In Proceedings of the IEEE international conf. on acoustic, speech and signal processing (ICASSP) (pp. 581–585). Google Scholar
  20. Özer, H., & Sankur, B. (2005). An SVD based audio watermarking technique. In Proceedings of the IEEE 13th conference on signal processing and communications applications (pp. 452–455). Google Scholar
  21. Pratt, W. K. (1991). Digital image processing. New York: Wiley. MATHGoogle Scholar
  22. Prochazka, A., Uhlir, J., Rayner, P. J. W., & Kingsbury, N. J. (1998). Signal analysis and prediction. Basel: Birkhauser. MATHGoogle Scholar
  23. Qian, Q., Chen, Z., & Yuan, Z. (2008). Video compression and encryption based-on multiple chaotic system. In Proceedings of the 3rd international conference on innovative computing information and control (ICICIC’08). Google Scholar
  24. Sun, X., Liu, J., Sun, J., Zhang, Q., & Ji, W. (2008). A robust image watermarking scheme based-on the relationship of SVD. In Proceedings of the international conference on intelligent information hiding and multimedia signal processing. Google Scholar
  25. Usman, K., Juzojil, H., & Nakajimal, I. (2007). Medical image encryption based on pixel arrangement and random permutation for transmission security. In Proceedings of the 9th international conference on e-health networking, application and services (pp. 244–247). Google Scholar
  26. Walker, J. S. (1999). A primer on wavelets and their scientific applications. Boca Raton: CRC Press. MATHGoogle Scholar
  27. Wang, S., Sekey, A., & Gersho, A. (1992). An objective measure for predicting subjective quality of speech coders. IEEE Journal on Selected Areas in Communication, 10(5), 819–829. CrossRefGoogle Scholar
  28. Wang, X., Qi, W., & Niu, P. (2007). A new adaptive digital audio watermarking based on support vector regression. IEEE Transactions on Audio, Speech, and Language Processing, 15(8), 2270–2277. CrossRefGoogle Scholar
  29. Wornell, G. W. (1996). Emerging applications of multirate signal processing and wavelets in digital communications. Proceedings of the IEEE, 84(4), 586–603. CrossRefGoogle Scholar
  30. Xiang, S., & Huang, J. (2007). Histogram-based audio watermarking against time-scale modification and cropping attacks. IEEE Transactions on Multimedia, 9(7), 1357–1372. CrossRefGoogle Scholar
  31. Yang, W., Benbouchta, M., & Yantorno, R. (1998). Performance of the modified bark spectral distortion as an objective speech quality measure. In Proceedings of the IEEE international conf. on acoustic, speech and signal processing (ICASSP), Washington, USA (Vol. 1, pp. 541–544). Google Scholar
  32. Zhu, X., Zhao, J., & Xu, H. (2006). A digital watermarking algorithm and implementation based on improved SVD. In Proceedings of the IEEE 18th international conference on pattern recognition (ICPR’06). Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Electronics and Electrical Communications, Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt

Personalised recommendations