High Payload Audio Watermarking Using Sparse Coding with Robustness to MP3 Compression

  • Mohamed Waleed Fakhr
Part of the Communications in Computer and Information Science book series (CCIS, volume 381)


A high payload audio watermarking technique is proposed based on the compressed sensing and sparse coding framework, with robustness to MP3 128kbps and 64kbps compression attacks. The binary watermark is a sparse vector with one non-zero element that takes a positive or negative sign based on the bit value to be encoded. A Gaussian random dictionary maps the sparse watermark to a random watermark embedding vector that is selected adaptively for each audio frame to maximize robustness to the MP3 attack. At the decoder, the Basis Pursuit Denoising algorithm (BPDN) is used to extract the embedded watermark sign. High payloads of (689, 1378 and 2756) bps are achieved with %BER of (0.3%, 0.5% and 1%) and (0.1%, 0.3% and 0.5%) for 64kbps and 128kbps MP3 compression attacks respectively. The signal to embedding noise ratio is kept in the range of 27-30 dB in all cases.


Sparse Coding Compressed Sensing Audio Watermarking MP3 Audio Robust Watermarking Basis Pursuit Denoising (BPDN) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Noriega, R.M., Nakano, M., Kurkoski, B., Yamaguchi, K.: High Payload Audio Watermarking: toward Channel Characterization of MP3 Compression. Journal of Information Hiding and Multimedia Signal Processing 2(2), 91–107 (2011)Google Scholar
  2. 2.
    Vivekananda, B.K., Indranil, S., Abhijit, D.: An Audio Watermarking Scheme using Singular Value Decomposition and Dither-Modulation Quantization. Multimedia Tools and Applications Journal 52(2-3), 369–383 (2011)CrossRefGoogle Scholar
  3. 3.
    Dhavale, S.V., Deodhar, R.S., Patnaik, L.M.: Walsh Hadamard Transform Based Blind Watermarking for Digital Audio Copyright Protection. In: Das, V.V., Thankachan, N. (eds.) CIIT 2011. CCIS, vol. 250, pp. 469–475. Springer, Heidelberg (2011)Google Scholar
  4. 4.
    Yang, H., Bao, D., Wang, X., Niu, P.: A Robust Content Based Audio Watermarking using UDWT and Invariant Histogram. Multimedia Tools and Applications Journal (November 2010)Google Scholar
  5. 5.
    El Hamdouni N., Adib A., Labri S., Torki M.: A Blind Digital Audio Watermarking Scheme Based on EMD and UISA Techniques. Multimedia Tools and Applications Journal (January 2012) Google Scholar
  6. 6.
    Tewari, T.K., Saxena, V., Gupta, J.P.: Audio Watermarking: Current State of Art and Future Objectives. International Journal of Digital Content Technology and Applications 5(7), 306–313 (2011)CrossRefGoogle Scholar
  7. 7.
    Datta, K., Gupta, I.S.: Partial Encryption and Watermarking Scheme for Audio Files with Controlled Degradation of Quality. Multimedia Tools and Applications, Journal (2012)Google Scholar
  8. 8.
    Ercelebi, E., Batakci, L.: Audio watermarking Scheme Based on Embedding Strategy in Low Frequency Components with a Binary Image. Digital Signal Processing 19(2), 265–277 (2009)CrossRefGoogle Scholar
  9. 9.
    Orsdemir, A., Altun, H.O., Sharma, G., Bocko, M.F.: On the Security and Robustness of Encryption via Compressed Sensing. In: IEEE Military Communication Conference MILCOM 2008, pp. 1–7 (2008)Google Scholar
  10. 10.
    Candès, E., Tao, T.: Decoding by Linear Programming. IEEE Transaction on Information Theory 51(12), 4203–4215 (2005)zbMATHCrossRefGoogle Scholar
  11. 11.
    Candès, E., Randall, P.: Highly Robust Error Correction by Convex Programming. IEEE Transaction on Information Theory 54(7) (2006)Google Scholar
  12. 12.
    Laska, J., Davenport, M., Baraniuk, R.: Exact Signal Recovery from Sparsely Corrupted Measurements through the Pursuit of Justice. In: Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, California (2009)Google Scholar
  13. 13.
  14. 14.
    Gemmeke, J.F., Virtanen, T., Hurmalainen, A.: Examplar Based Sparse Representations for Noise Robust Automatic Speech Recognition. IEEE Trans. Audio, Speech and Language Processing 19(9), 2067–2080 (2011)CrossRefGoogle Scholar
  15. 15.
    Sprechman, P., Sapiro, G.: Dictionary Learning and Sparse Coding for Unsupervised Clustering. In: ICASSP 2010, pp. 2042–2045 (2010)Google Scholar
  16. 16.
    Wright, J., Yi, M., Mairal, J., Sapiro, G., Huang, T., Yan, S.: Sparse Representation for Computer Vision and Pattern Recognition. Proc. of IEEE 98(6), 1031–1044 (2010)CrossRefGoogle Scholar
  17. 17.
    Sheikh, M., Baraniuk, R.: Blind Error-Free Detection of Transform-Domain Watermarks. In: IEEE Int. Conf. on Image Processing (ICIP), San Antonio, Texas, vol. 5, pp. V-453–V-456 (September 2007)Google Scholar
  18. 18.
    Tagliasacchi, M., Valenzise, G., Tubaro, S.: Hash-Based Identification of Sparse Image Tampering. IEEE Transactions on Image Processing 18(11), 2491–2504 (2009)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Valenzise, G., Prandi, G., Tagliasacchi, M., Sarti, A.: Identification of Sparse Audio Tampering using Distributed Source Coding and Compressive Sensing Techniques. Eurasip Journal on Image and Video Processing 2009, 1–13 (2009)CrossRefGoogle Scholar
  20. 20.
    Fakhr, M.W.: Robust Watermarking using Compressed Sensing Framework with Application to MP3. International Journal of Multimedia and its Applications, IJMA 4(6), 27–43 (2012)CrossRefGoogle Scholar
  21. 21.
    Fakhr, M.W.: Sparse Watermark Embedding and Recovery using Compressed Sensing Framework for Audio Signals. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Sanya, China, pp. 535–539 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mohamed Waleed Fakhr
    • 1
  1. 1.Electrical and Electronics DepartmentUniversity of BahrainManamaBahrain

Personalised recommendations