Advertisement

An imperceptible, robust, and high payload capacity audio watermarking scheme based on the DCT transformation and Schur decomposition

  • Huda Karajeh
  • Mahmoud Maqableh
Article
  • 20 Downloads

Abstract

This paper proposes a novel audio watermarking scheme based on the discrete cosine transform (DCT) and Schur decomposition. The proposed scheme uses the DCT transformation to increase robustness and the Schur decomposition to achieve perceptual transparency. The proposed scheme first applies the DCT transformation to the original audio signal and then applies the Schur decomposition to the mid-frequency band of the DCT coefficients that generate two matrices (U and S). The watermark bits are embedded into the diagonal elements of the triangular matrix S. The Schur decomposition increases the perceptual transparency and the DCT transformation increases robustness of the proposed audio watermarking scheme by effectively resisting several types of audio signal attacks. The imperceptibility of the proposed watermarking scheme is measured subjectively using subjective difference grades (SDG) and objectively using signal-to-noise ratio (SNR) and objective difference grades (ODG) metrics. Its robustness is evaluated against several types of attacks in terms of NC and BER for different types of audio. The resulting of payload capacity, SNR, NC, and BER are as high as 516.26 bps, 77.95, 0.05326, and 0.9727, respectively. Experimental results confirm the proposed scheme is efficient, imperceptible, and robust with a high payload capacity and no effecting audio signal.

Keywords

Audio watermark DCT Schur Payload capacity Signal-to-noise ratio Signal processing 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Hu, H. T., Hsu, L. Y., & Chou, H. H. (2014). Perceptual-based DWPT–DCT framework for selective blind audio watermarking. Signal Processing, 105, 316–627.  https://doi.org/10.1016/j.sigpro.2014.05.003.CrossRefGoogle Scholar
  2. 2.
    Peng, H., Li, B., Luo, X., Wang, J., & Zhang, Z. (2013). A learning-based audio watermarking scheme using kernel Fisher discriminant analysis. Digital Signal Processing, 23(1), 382–389.  https://doi.org/10.1016/j.dsp.2012.08.006.MathSciNetCrossRefGoogle Scholar
  3. 3.
    Hu, H. T., & Hsu, L. Y. (2015). Robust, transparent and high-capacity audio watermarking in DCT domain. Signal Processing, 109, 226–235.  https://doi.org/10.1016/j.sigpro.2014.11.011.CrossRefGoogle Scholar
  4. 4.
    Liu, J., & She, K. (2012). A hybrid approach of DWT and DCT for rational dither modulation watermarking. Circuits Systems and Signal Processing, 31(2), 797–811.  https://doi.org/10.1007/s00034-011-9331-8.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Tsai, H.-H., Cheng, J.-S., & Yu, P.-T. (2003). Audio watermarking based on HAS and neural networks in DCT domain. EURASIP Journal on Advances in Signal Processing, 2003(3), 252–263.  https://doi.org/10.1155/S1110865703208027.CrossRefGoogle Scholar
  6. 6.
    Dhar, P. K., & Shimamura, T. (2017). Blind audio watermarking in transform domain based on singular value decomposition and exponential-log operations. Radioengineering, 26(2), 552–561.  https://doi.org/10.13164/re.2017.0552.CrossRefGoogle Scholar
  7. 7.
    Hu, H. T., Chang, J. R., & Lin, S. J. (2018). Synchronous blind audio watermarking via shape configuration of sorted LWT coefficient magnitudes. Signal Processing, 147, 190–202.  https://doi.org/10.1016/j.sigpro.2018.02.001.CrossRefGoogle Scholar
  8. 8.
    Tewari, T. K., Saxena, V., & Gupta, J. P. (2014). A digital audio watermarking scheme using selective mid band DCT coefficients and energy threshold. International Journal of Speech Technology, 17(4), 365–371.  https://doi.org/10.1007/s10772-014-9234-8.CrossRefGoogle Scholar
  9. 9.
    Al-Haj, A. (2014). An imperceptible and robust audio watermarking algorithm. EURASIP Journal on Audio, Speech, and Music Processing, 2014(37), 1–12.  https://doi.org/10.1186/s13636-014-0037-2.CrossRefGoogle Scholar
  10. 10.
    Qasim, A. F., Meziane, F., & Aspin, R. (2018). Digital watermarking: Applicability for developing trust in medical imaging workflows state of the art review. Computer Science Review, 27, 45–60.  https://doi.org/10.1016/j.cosrev.2017.11.003.MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Bassia, P., Pitas, I., & Nikolaidis, N. (2001). Robust audio watermarking in the time domain. IEEE Transactions on Multimedia, 3(2), 232–241.CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Lie, W.-N., & Chang, L.-C. (2006). Robust and high-quality time-domain audio watermarking based on low-frequency amplitude modification. IEEE Transactions on Multimedia, 8(1), 46–59.CrossRefGoogle Scholar
  14. 14.
    Wang, H., Nishimura, R., Suzuki, Y., & Mao, L. (2008). Fuzzy self-adaptive digital audio watermarking based on time-spread echo hidinge. Applied Acoustics, 69(10), 868–874.CrossRefGoogle Scholar
  15. 15.
    Aparna, J. R., & Ayyappan, S. (2014). Comparison of digital watermarking techniques department of computer science. In 2014 International conference on computation of power, energy, information and communication (ICCPEIC) (pp. 87–92). Chennai.Google Scholar
  16. 16.
    Singh, P., & Chadha, R. (2013). A survey of digital watermarking techniques, applications and attacks. International Journal of Engineering and Innovative Technology (IJEIT), 2(9), 165–175.  https://doi.org/10.1109/INDIN.2005.1560462.CrossRefGoogle Scholar
  17. 17.
    Kavadia, C., & Lodha, A. (2013). A review on spatial & transform domain digital watermarking techniques. International Journal of Advanced Research in Computer Science, 4(3), 20–22.Google Scholar
  18. 18.
    Rajab, L., Al-khatib, T., & Al-haj, A. (2015). A blind DWT–Schur based digital video watermarking technique. Journal of Software Engineering and Applications, 8(1), 224–233.CrossRefGoogle Scholar
  19. 19.
    Hu, H. T., & Hsu, L. Y. (2017). Incorporating spectral shaping filtering into DWT-based vector modulation to improve blind audio watermarking. Wireless Personal Communications, 94(2), 221–240.  https://doi.org/10.1007/s11277-016-3178-z.CrossRefGoogle Scholar
  20. 20.
    Lei, B., Soon, I. Y., & Tan, E. L. (2013). Robust SVD-based audio watermarking scheme with differential evolution optimization. IEEE Transactions on Audio, Speech and Language Processing, 21(11), 2368–2378.  https://doi.org/10.1109/TASL.2013.2277929.CrossRefGoogle Scholar
  21. 21.
    Yoosuf, S., & Alex, A. M. (2015). Audio watermarking using colour image based on EMD and DCT. International Journal of Advanced Research in Computer and Communication Engineering, 4(6), 363–367.  https://doi.org/10.17148/IJARCCE.2015.4679.CrossRefGoogle Scholar
  22. 22.
    Maha, C., Maher, E., Mohamed, K., & Chokri, B. A. (2010). DCT based blind audio watermarking scheme. In 2010 International conference on signal processing and multimedia applications (SIGMAP) (pp. 139–144). Athens, Greece.Google Scholar
  23. 23.
    Roy, S., Sarkar, N., Chowdhury, A. K., & Iqbal, S. M. A. (2015). An efficient and blind audio watermarking technique in DCT domain. In 2015 18th international conference on computer and information technology, ICCIT (pp. 362–367). Dhaka.  https://doi.org/10.1109/iccitechn.2015.7488097.
  24. 24.
    Charfeddine, M., El’Arbi, M., & Amar, C. B. (2014). A new DCT audio watermarking scheme based on preliminary MP3 study. Multimedia Tools and Applications, 70(3), 1521–1557.  https://doi.org/10.1007/s11042-012-1167-0.CrossRefGoogle Scholar
  25. 25.
    Li, W., Xue, X., & Lu, P. (2006). Localized audio watermarking technique robust modification, against time-scale. IEEE Transactions on Multimedia, 2(1), 60–69.CrossRefGoogle Scholar
  26. 26.
    Tachibana, R., Shimizu, S., Kobayashi, S., & Nakamura, T. (2002). An audio watermarking method using a two-dimensional pseudo-random array. Signal Process, 82(10), 1455–1469.CrossRefGoogle Scholar
  27. 27.
    Wang, X.-Y., Niu, P.-P., & Yang, H.-Y. (2009). A robust digital audio watermarking based on statistics characteristics. Pattern Recognition, 42(11), 3057–3064.CrossRefGoogle Scholar
  28. 28.
    Wu, S., Huang, J., Huang, D., & Shi, Y. Q. (2005). Efficiently self-synchronized audio watermarking for assured audio data transmission. IEEE Transactions on Broadcasting, 51(1), 69–76.CrossRefGoogle Scholar
  29. 29.
    Wang, X., Wang, P., Zhang, P., Xu, S., & Yang, H. (2013). Norm-space, aadaptive, and blind audio watermarking algorithm by discrete wavelet transform. Signal Processing, 93(4), 913–922.CrossRefGoogle Scholar
  30. 30.
    Dhar, P. K., & Shimamura, T. (2015). Blind SVD-based audio watermarking using entropy and log-polar transformation. Journal of Information Security and Applications, 20, 74–83.  https://doi.org/10.1016/j.jisa.2014.10.007.CrossRefGoogle Scholar
  31. 31.
    Bhat, V., Sengupta, K. I., & Das, A. (2010). An adaptive audio watermarking based on the singular value decomposition in the wavelet domain. Digital Signal Processing, 20(6), 1547–1558.CrossRefGoogle Scholar
  32. 32.
    Lei, B., Soon, I. Y., Zhou, F., Li, Z., & Lei, H. (2012). A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition. Signal Processing, 92(9), 1985–2001.CrossRefGoogle Scholar
  33. 33.
    Bansal, N., Bansal, A., Deolia, V., & Pathak, P. (2015). Comparative Analysis of LSB, DCT and DWT for Digital Watermarking. In 2nd international conference on computing for sustainable global development (INDIACom) (pp. 40–45). Mathura, India.  https://doi.org/10.1109/eesco.2015.7253657.
  34. 34.
    Hu, H. T., & Hsu, L. Y. (2017). Supplementary schemes to enhance the performance of DWT-RDM-based blind audio watermarking. Circuits, Systems, and Signal Processing, 36(5), 1890–1911.  https://doi.org/10.1007/s00034-016-0383-7.CrossRefGoogle Scholar
  35. 35.
    Singh, D., & Singh, S. K. (2017). DWT-SVD and DCT based robust and blind watermarking scheme for copyright protection. Multimedia Tools and Applications, 76(11), 13001–13024.  https://doi.org/10.1007/s11042-016-3706-6.CrossRefGoogle Scholar
  36. 36.
    Roy, S., & Pal, A. K. (2017). A robust blind hybrid image watermarking scheme in RDWT–DCT domain using Arnold scrambling. Multimedia Tools and Applications, 76(3), 3577–3616.  https://doi.org/10.1007/s11042-016-3902-4.CrossRefGoogle Scholar
  37. 37.
    Abd El-Samie, F. E. (2009). An efficient singular value decomposition algorithm for digital audio watermarking. International Journal of Speech Technology, 12(1), 27–45.  https://doi.org/10.1007/s10772-009-9056-2.CrossRefGoogle Scholar
  38. 38.
    Hu, H. T., & Chang, J. R. (2017). Efficient and robust frame-synchronized blind audio watermarking by featuring multilevel DWT and DCT. Cluster Computing, 20(1), 805–816.  https://doi.org/10.1007/s10586-017-0770-2.CrossRefGoogle Scholar
  39. 39.
    Šego, V. (2014). The hyperbolic Schur decomposition. Linear Algebra and its Applications, 440(1), 90–110.  https://doi.org/10.1016/j.laa.2013.10.037.MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Mohammad, A. A. (2012). A new digital image watermarking scheme based on Schur decomposition. Multimedia Tools and Applications, 59(3), 851–883.  https://doi.org/10.1007/s11042-011-0772-7.CrossRefGoogle Scholar
  41. 41.
    Su, Q., Niu, Y., Liu, X., & Zhu, Y. (2012). Embedding color watermarks in color images based on Schur decomposition. Optics Communications, 285(7), 1792–1802.  https://doi.org/10.1016/j.optcom.2011.12.065.CrossRefGoogle Scholar
  42. 42.
    Looperman Pro Audio Resources Community Forums, https://www.looperman.com. Accessed date: 15 July, 2017. (n.d.).
  43. 43.
    Kabal, P. (2003). An examination and interpretation of ITU-R BS. 1387: Perceptual evaluation of audio quality. Montreal: McGill University.Google Scholar
  44. 44.
    Subir, & Joshi, A. M. (2016). DWT-DCT based blind audio watermarking using Arnold scrambling and Cyclic codes. In 3rd international conference on signal processing and integrated networks (SPIN) (pp. 79–84). Noida.  https://doi.org/10.1109/spin.2016.7566666.
  45. 45.
    Hsu, L. Y., & Hu, H. T. (2015). Blind image watermarking via exploitation of inter-block prediction and visibility threshold in DCT domain. Journal of Visual Communication and Image Representation, 32, 130–143.  https://doi.org/10.1016/j.jvcir.2015.07.017.CrossRefGoogle Scholar
  46. 46.
    Hu, H. T., Chen, S. H., & Hsu, L. Y. (2014). Incorporation of perceptually energy-compensated qim into dwt-dct based blind audio watermarking. In Proceedings2014 10th international conference on intelligent information hiding and multimedia signal processing (pp. 748–752). Kitakyushu, Japan.  https://doi.org/10.1109/iih-msp.2014.191.
  47. 47.
    Dong, L., Yan, Q., Lv, Y., & Deng, S. (2017). Full band watermarking in DCT domain with Weibull model. Multimedia Tools and Applications, 76(2), 1983–2000.  https://doi.org/10.1007/s11042-015-3115-2.CrossRefGoogle Scholar
  48. 48.
    Yang, Y., Lei, M., Liu, X., Qu, Z., & Wang, C. (2016). Novel zero-watermarking scheme based on DWT–DCT. China Communications, 13(7), 122–126.CrossRefGoogle Scholar
  49. 49.
    Pattanshetti, P., Dongaonkar, S., & Karpe, S. (2015). Digital watermarking in audio using least significant bit and discrete cosine transform. International Journal of Computer Science and Information Technologies (IJCSIT), 6(4), 3688–3692.Google Scholar
  50. 50.
    Kaur, N., & Kaur, U. (2013). Audio watermarking using arnold transformation with DWT-DCT. International Journal of Computer Science Engineering (IJCSE), 2(6), 286–294.Google Scholar
  51. 51.
    Zhang, J. (2015). Audio dual watermarking scheme for copyright protection and content authentication. International Journal of Speech Technology, 18(3), 443–448.  https://doi.org/10.1007/s10772-015-9287-3.CrossRefGoogle Scholar
  52. 52.
    Deokar, S. M., & Dhaigude, B. (2015). Blind audio watermarking based on discrete wavelet and cosine transform. In 2015 international conference on industrial instrumentation and control (ICIC) (pp. 264–268). Pune.  https://doi.org/10.1109/iic.2015.7150750.
  53. 53.
    Dutta, M. K., Gupta, P., & Pathak, V. K. (2014). A perceptible watermarking algorithm for audio signals. Multimedia Tools and Applications, 73(2), 691–713.  https://doi.org/10.1007/s11042-011-0945-4.CrossRefGoogle Scholar
  54. 54.
    Deb, K., Rahman, M. A., Sultana, K. Z., Sarker, M. I. H., & Chong, U.-P. (2014). DCT and DWT based robust audio watermarking scheme for copyright protection. The Journal of Korea Institute of Signal Processing and Systems, 15(1), 1–9.Google Scholar
  55. 55.
    Milaš, I., Radović, B., & Janković, D. (2016). A new audio watermarking method with optimal detection. In 5th Mediterranean conference on embedded computing (pp. 116–119). Bar.  https://doi.org/10.1109/meco.2016.7525717.
  56. 56.
    Al-Haj, A. (2014). A dual transform audio watermarking algorithm. Multimedia Tools and Applications, 73(3), 1897–1912.  https://doi.org/10.1007/s11042-013-1645-z.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Computer Information Systems Department, King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.Management Information Systems Department, School of BusinessThe University of JordanAmmanJordan

Personalised recommendations