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Multimedia Tools and Applications

, Volume 77, Issue 19, pp 25607–25627 | Cite as

Robust audio watermarking algorithm based on DWT using Fibonacci numbers

  • Ali Akbar Attari
  • Ali Asghar Beheshti Shirazi
Article

Abstract

This paper presents a blind and robust audio watermarking algorithm developed based on Fibonacci numbers properties and the discrete wavelet transform (DWT) advantages. The method embeds watermark bits in the 6th level approximation subband of DWT at which there is less sensitivity of the human auditory system. The key idea is dividing the 6th level approximation coefficients into small frames and modifying their magnitude based on Fibonacci numbers and watermark bit values. The proposed watermarking method demonstrates a superior robustness against different common attacks (i.e., Gaussian noise addition, Low-pass filter, Resampling, Requantizing, MP3 compression, Amplitude scaling, Echo addition, Time shift, and Cropping). Compared to recently developed methods, the proposed algorithm is much more robust against the most common attacks with capacity as high as 686 bits per second. The results of PEAQ testing verify the quality of watermarked audio signal without significant perceptual distortion. The algorithm allows flexibility in audio watermark algorithm to achieve a balance between robustness and imperceptibility while the capacity is maintained constant by choosing various kinds of sequence.

Keywords

Blind audio watermarking Fibonacci numbers Discrete wavelet transform Human auditory masking 

References

  1. 1.
    Akansu AN, Richard AH (2001) Multiresolution signal decomposition: transforms, subbands, and wavelets. Academic Press, BostonzbMATHGoogle Scholar
  2. 2.
    Al-Haj A (2014) An imperceptible and robust audio watermarking algorithm. EURASIP J Audio, Speech, Music Process 2014(1):37CrossRefGoogle Scholar
  3. 3.
    Attari AA, Asghar BeheshtiShirazi A (2017) Robust and blind audio watermarking in wavelet domain. In: Proc. Int. Conf. Graph. Signal Process. - ICGSP ‘17. ACM Press, Singapore, Singapore, pp 69–73Google Scholar
  4. 4.
    Bhat KV, Sengupta I, Das A (2010) An adaptive audio watermarking based on the singular value decomposition in the wavelet domain. ELSEVIER - Digit Signal Process 20(6):1547–1558CrossRefGoogle Scholar
  5. 5.
    Can YS, Alagoz F, Burus ME (2014) A novel spread spectrum digital audio watermarking technique. J Adv Comput Networks 2(1):6–9CrossRefGoogle Scholar
  6. 6.
    Charfeddine M, El’arbi M, Amar CB (2014) A new DCT audio watermarking scheme based on preliminary MP3 study. Multimed Tools Appl 70(3):1521–1557CrossRefGoogle Scholar
  7. 7.
    Dhar PK, Shimamura T (2014) Audio watermarking in transform domain based on singular value decomposition and Cartesian-polar transformation. Int J Speech Technol 17(2):133–144CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Fallahpour M, Megias D (2011) High capacity audio watermarking using the high frequency band of the wavelet domain. Multimed Tools Appl 52(2-3):485–498CrossRefGoogle Scholar
  10. 10.
    Fallahpour M, Megias D (2015) Audio watermarking based on Fibonacci numbers. IEEE/ACM Trans Audio, Speech, Lang Process 23(8):1273–1282CrossRefGoogle Scholar
  11. 11.
    Fugal DL (2009) Conceptual wavelets in digital signal processing. Space and Signals Technical Publishing, San DiegoGoogle Scholar
  12. 12.
    Hu HT, Chang JR (2017) Efficient and robust frame-synchronized blind audio watermarking by featuring multilevel DWT and DCT. Clust Comput 20(1):805–816CrossRefGoogle Scholar
  13. 13.
    Hu HT, Chen WH (2012) A dual cepstrum-based watermarking scheme with self-synchronization. Signal Process 92(4):1109–1116CrossRefGoogle Scholar
  14. 14.
    Hu HT, Hsu LY (2015) A DWT-based rational dither modulation scheme for effective blind audio watermarking. Circuits, Syst Signal Process 35(2):553–572CrossRefGoogle Scholar
  15. 15.
    Hu HT, Chou HH, Yu C, Hsu LY (2014a) Incorporation of perceptually adaptive QIM with singular value decomposition for blind audio watermarking. EURASIP J Adv Signal Process 2014(1):1–12Google Scholar
  16. 16.
    Hu HT, Hsu LY, Chou HH (2014b) Variable-dimensional vector modulation for perceptual-based DWT blind audio watermarking with adjustable payload capacity. Digit Signal Process A Rev J 31:115–123CrossRefGoogle Scholar
  17. 17.
    Jeyhoon M, Asgari M, Ehsan L, Jalilzadeh SZ (2016) Blind audio watermarking algorithm based on DCT, linear regression and standard deviation. Multimed Tools Appl 76(3):3343–3359CrossRefGoogle Scholar
  18. 18.
    Kabal P (2002) An examination and interpretation of ITU-R BS. 1387: perceptual evaluation of audio quality. TSP Lab Technical Report, Dept. Electrical & Computer Engineering, McGill University, Montreal, pp 1–89Google Scholar
  19. 19.
    Lei BY, Soon Y, Li Z (2011) Blind and robust audio watermarking scheme based on SVD–DCT. Signal Process 91(8):1973–1984CrossRefzbMATHGoogle Scholar
  20. 20.
    Lei B, Soon Y, Zhou F, Li Z, Lei H (2012) A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition. Signal Process 92(9):1985–2001CrossRefGoogle Scholar
  21. 21.
    Lin Y, Abdulla WH (2015) Audio watermark: a comprehensive foundation using MATLAB. Springer International PublishingGoogle Scholar
  22. 22.
    Liu SC, Lin SD (2006) BCH code-based robust audio watermarking in the cepstrum domain. J Inf Sci Eng 22(3):535–543MathSciNetGoogle Scholar
  23. 23.
    Megias D, Serra-Ruiz J, Fallahpour M (2010) Efficient self-synchronised blind audio watermarking system based on time domain and FFT amplitude modification. Signal Process 90(12):3078–3092CrossRefzbMATHGoogle Scholar
  24. 24.
  25. 25.
    Seok J (2012) Audio watermarking using independent component analysis. J Inf Commun Converg Eng 10(2):175–180Google Scholar
  26. 26.
    Tewari TK (2015) Novel Techniques for Improving the Performance of Digital Audio Watermarking for Copyright Protection. Ph.D. dissertation, Dept. Computer Science Engineering & Information Technology, Jaypee Institute of Information Technology, NoidaGoogle Scholar
  27. 27.
    Wang J (2011) New digital audio watermarking algorithms for copyright protection. Ph.D. dissertation, Dept. Computer Science, National University of Ireland, Maynooth, Co. Kildare, IrelandGoogle Scholar
  28. 28.
    Wang H, Nishimura R, Suzuki Y, Mao L (2008) Fuzzy self-adaptive digital audio watermarking based on time-spread echo hiding. Appl Acoust 69(10):868–874CrossRefGoogle Scholar
  29. 29.
    Wang X, Wang P, Zhang P, Xu S, Yang H (2013) A norm-space, adaptive, and blind audio watermarking algorithm by discrete wavelet transform. Signal Process 93(4):913–922CrossRefGoogle Scholar
  30. 30.
    Xiang Y, Natgunanathan I, Guo S, Zhou W, Nahavandi S (2014) Patchwork-based audio watermarking method robust to de-synchronization attacks. IEEE/ACM Trans Audio, Speech, Lang Process 22(9):1413–1423CrossRefGoogle Scholar
  31. 31.
    Xiang Y, Hua G, Yan B (2017) Digital audio watermarking: fundamentals, techniques and challenges. Springer, SingaporeCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ali Akbar Attari
    • 1
  • Ali Asghar Beheshti Shirazi
    • 1
  1. 1.School of Electrical EngineeringIran University of Science & Technology (IUST)TehranIran

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