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Robust audio watermarking using frequency-selective spread spectrum

Robust audio watermarking using frequency-selective spread spectrum

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© The Institution of Engineering and Technology
Published

A novel audio watermarking scheme based on frequency-selective spread spectrum (FSSS) technique is presented. Unlike most of the existing spread spectrum (SS) watermarking schemes that use the entire audible frequency range for watermark embedding, the proposed scheme randomly selects subband(s) signal(s) of the host audio signal for watermark embedding. The proposed FSSS scheme provides a natural mechanism to exploit the band-dependent frequency-masking characteristics of the human auditory system to ensure the fidelity of the host audio signal and the robustness of the embedded information. Key attributes of the proposed scheme include reduced host interference in watermark detection, better fidelity, secure embedding and improved multiple watermark embedding capability. To detect the embedded watermark, two blind watermark detection methods are examined, one based on normalised correlation and the other based on estimation correlation. Extensive simulation results are presented to analyse the performance of the proposed scheme for various signal manipulations and standard benchmark attacks. A comparison with the existing full-band SS-based schemes is also provided to show the improved performance of the proposed scheme.

Inspec keywords: audio signal processing; correlation methods; watermarking

Other keywords: blind watermark detection methods; normalised correlation; host audio signal; robust audio watermarking; band-dependent frequency-masking characteristics; frequency-selective spread spectrum technique; human auditory system; estimation correlation

Subjects: Speech and audio coding; Digital signal processing; Data security; Signal processing and detection

References

    1. 1)
      • MP3's: ‘Friend or foe?’,http://www.freeessays.cc/db/48/tvh112.shtml accessed on 23 June 2008.
    2. 2)
      • Y. Li , D. Powers , J. Peach , N. Mastorakis . (2000) Comparison of blind source separation algorithms, Advances in neural networks and applicaitons.
    3. 3)
      • http://sound.media.mit.edu/mpeg4/audio/sqam/, accessed on June 23, 2008.
    4. 4)
      • M.D. Swanson , B. Zhu , A.H. Tewfik , L. Boney . Robust audio watermarking using perceptual masking. Signal Process. , 3 , 337 - 355
    5. 5)
      • T. Sorer , K. Brandenburg . Constraints of filter banks used for perceptual measurements. J. Audio Eng. Soc. , 3 , 107 - 115
    6. 6)
      • P. Noll . MPEG digital audio coding. IEEE Signal Process. Mag. , 5 , 59 - 81
    7. 7)
      • A. Hyvarinen . Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood. Neurocomputing , 49 - 67
    8. 8)
      • H.V. Poor . (1988) An introduction to signal detection and estimation.
    9. 9)
      • D. Pan . A tutorial on MPEG/audio compression. IEEE Multimedia Mag. , 2 , 60 - 74
    10. 10)
      • F. P-Gonzalez , F. Balado , J.R. Hernndez . Performance analysis of existing and new methods for data hiding with known-host information in additive channels. IEEE Trans. Signal Process. , 4 , 960 - 980
    11. 11)
      • A. Hyvärinen . Survey on independent component analysis. Neural Comput. Surv. , 94 - 128
    12. 12)
      • T.M. Cover , J.A. Thomas . (1991) Elements of information tehoey.
    13. 13)
      • Malik, H., Khokhar, A., Ansari, R.: `Improved watermark detection for spread-spectrum based watermarking using independent component analysis', Proc. 5th ACM Workshop On Digital Rights Management (DRM‘05), November 2005, p. 102–111.
    14. 14)
      • Malik, H., Khokhar, A., Ansari, R.: `Robust audio watermarking using frequency selective spread spectrum theory', Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP'04), May 2004, p. 385–388.
    15. 15)
      • Gribonval, R., Benaroya, L., Vincent, E., Fevotte, C.: `Proposals for performance measurement in source separation', Proc. 4th Int. Sym. Independent Component Analysis and Blind Source Separation, April 2003, p. 763–768.
    16. 16)
      • Wu, C.-P., Su, P.-C., Kuo, C.-C.J.: `Robust audio watermarking for copyright protection', Proc. SPIE's 44th Ann. Meet. Adv. Sig. Proc. Alg. Arch. Impl. IX (SD39), July 1999, 3807, p. 387–397.
    17. 17)
      • R.B. Wolfgang , C.I. Podilchuk , E.J. Delp . Perceptual watermarks for digital images and video. Proc. IEEE , 7 , 1108 - 1126
    18. 18)
      • I.J. Cox , J. Kilian , T. Leighton , T. Shamoon . secure spread spectrum watermarking for multimedia. IEEE Trans. Image Process. , 12 , 1673 - 1687
    19. 19)
      • H.S. Malvar , D.A.F. Florencio . Improved spread spectrum: a new modulation technique for robust watermarking. IEEE Trans. Signal Process. , 4 , 898 - 905
    20. 20)
      • ITU-R Rec. BS.1116(rev. 1): ‘Methods for the subjective assessment of small impairments in audio systems including multichannel sound systems’, Int. Telecommun. Union, 1997..
    21. 21)
      • B. Chen , I. Pitas , G.W. Wornell . Quantization index modulation: a class of provably good methods for digital watermarking and information embedding. IEEE Trans. Inf. Theory , 4 , 1423 - 1443
    22. 22)
      • M. Steinebach , A. Lang , J. Dittmann , F.A.P. Prtitcolas . StirMark benchmark: audio watermarking attacks based on lossy compression. Proc. SPIE Secur. Watermarking Multimedia , 79 - 90
    23. 23)
      • J.D. Johnston . Transform coding of audio signals using perceptual noise criteria. IEEE J. Sel. Areas Commun. , 2 , 314 - 323
    24. 24)
      • I. Cox , J. Bloom , M. Miller . (2001) Digital watermarking.
    25. 25)
      • M.H.M. Costa . Writing on dirty paper. IEEE Trans. Inf. Theory , 3 , 439 - 441
    26. 26)
      • P. Moulin , M.K. Mihcak . A framework for evaluating the data-hiding capacity of image sources. IEEE Trans. Image Process. , 9 , 1029 - 1042
    27. 27)
      • J. Eggers , B. Girod . (2002) Informed watermarking.
    28. 28)
      • Hansen, L.K., Petersen, K.B.: `Monoaural ICA of while noise mixture is hard', Proc. Sym ICA and BSS (ICA2003), 2003, p. 815–820.
    29. 29)
      • R.E. Zwicker , H. Fastl . (1999) Psychoacoustics: Facts and Models.
    30. 30)
      • D. Kirovski , H.S. Malvar . Spread spectrum watermarking of audio signals. IEEE Trans. Signal Proc. , 4 , 1020 - 1033
    31. 31)
      • StirMark Benchmark for Audio: available at http://amsl-smb.cs.uni-magdeburg.de/smfa/main.php, accessed on 23 June 2008.
    32. 32)
      • ITU-R Rec. BS.562-3, ‘Subjective Assessment of Sound Quality’, International Telecommunications Union, 1997..
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