Skip to main content
Log in

A novel SVD-based adaptive robust audio watermarking algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

To solve the copyright problem of audio data, many singular value decomposition (SVD)-based audio watermarking schemes have been proposed, however, most SVD-based schemes cannot improve the imperceptibility and robustness while guaranteeing a certain embedding capacity. Therefore, we propose a new SVD-based adaptive robust audio watermarking method. In this method, after framing the host audio signal, a discrete wavelet transform (DWT) is performed on each frame, and then the obtained DWT coefficients are divided into two segments using a sub-sampling operation, and the SVD is performed on these two segments and the mean value of the two singular values is calculated. Then the watermark bits are embedded by modifying the singular values of the two segments using differential embedding method. In the above watermark embedding process, the proposed adaptive method generates different sizes of embedding parameters according to the original signal features of each frame to minimize the degradation of perceived quality. During the watermark extraction process, the watermark can still be correctly extracted without the original audio signal and embedding parameters. The experimental results show that the scheme is more robust than existing audio watermarking schemes under various attacks with a certain embedding capacity guaranteed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availibility Statement

Data can be provided if the request is reasonable.

References

  1. Wang Y, Pan Y, Yan M, Su Z, Luan TH (2023) A survey on ChatGPT: AI-generated contents, challenges, and solutions

  2. Hu R, Xiang S (2021) Lossless robust image watermarking by using polar harmonic transform. Signal Process 179:107833

    Article  Google Scholar 

  3. Chen Y, Jia Z-G, Peng Y, Peng Y-X, Zhang D (2021) A new structure-preserving quaternion qr decomposition method for color image blind watermarking. Signal Process 185:108088

    Article  Google Scholar 

  4. Wang X-Y, Shen X, Tian J-L, Niu P-P, Yang H-Y (2022) Statistical image watermark decoder using high-order difference coefficients and bounded generalized gaussian mixtures-based hmt. Signal Process 192:108371

    Article  Google Scholar 

  5. Asikuzzaman M, Pickering MR (2018) An overview of digital video watermarking. IEEE Trans Circuits Syst Video Technol 28(9):2131–2153. https://doi.org/10.1109/TCSVT.2017.2712162

    Article  Google Scholar 

  6. Asikuzzaman M, Mareen H, Moustafa N, Choo K-KR, Pickering MR (2022) Blind camcording-resistant video watermarking in the dtcwt and svd domain. IEEE Access 10:15681–15698. https://doi.org/10.1109/ACCESS.2022.3146723

    Article  Google Scholar 

  7. Chen S, Malik A, Zhang X, Feng G, Wu H (2023) A fast method for robust video watermarking based on zernike moments. IEEE Trans Circ Syst Video Technol 1–1. https://doi.org/10.1109/TCSVT.2023.3281618

  8. Malvar HS, Florêncio DA (2003) Improved spread spectrum: A new modulation technique for robust watermarking. IEEE Trans Signal Process 51(4):898–905

    Article  MathSciNet  Google Scholar 

  9. Valizadeh A, Wang ZJ (2010) Correlation-and-bit-aware spread spectrum embedding for data hiding. IEEE Trans Inf Forensics Secur 6(2):267–282

    Article  Google Scholar 

  10. Zhang P, Xu S-Z, Yang H-Z (2012) Robust audio watermarking based on extended improved spread spectrum with perceptual masking. Int J Fuzzy Syst 14(2)

  11. Zhang X, Wang ZJ (2013) Correlation-and-bit-aware multiplicative spread spectrum embedding for data hiding. In: 2013 IEEE International workshop on information forensics and security (WIFS), pp 186–190 . IEEE

  12. Xiang Y, Natgunanathan I, Rong Y, Guo S (2015) Spread spectrum-based high embedding capacity watermarking method for audio signals. IEEE/ACM Trans Audio Speech Lang Process 23(12):2228–2237

    Article  Google Scholar 

  13. Xiang Y, Natgunanathan I, Peng D, Hua G, Liu B (2017) Spread spectrum audio watermarking using multiple orthogonal pn sequences and variable embedding strengths and polarities. IEEE/ACM Trans Audio Speech Lang Process 26(3):529–539

    Article  Google Scholar 

  14. Natgunanathan I, Xiang Y, Rong Y, Zhou W, Guo S (2012) Robust patchwork-based embedding and decoding scheme for digital audio watermarking. IEEE Trans Audio Speech Lang Process 20(8):2232–2239

    Article  Google Scholar 

  15. 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):1413–1423

    Article  Google Scholar 

  16. Natgunanathan I, Xiang Y, Hua G, Beliakov G, Yearwood J (2017) Patchwork-based multilayer audio watermarking. IEEE/ACM Trans Audio Speech Lang Process 25(11):2176–2187

    Article  Google Scholar 

  17. Liu Z, Huang Y, Huang J (2018) Patchwork-based audio watermarking robust against de-synchronization and recapturing attacks. IEEE Trans Inf Forensics Secur 14(5):1171–1180

    Article  Google Scholar 

  18. Vivekananda BK, Sengupta I, Das A (2010) An adaptive audio watermarking based on the singular value decomposition in the wavelet domain - sciencedirect. Digital Signal Process 20(6):1547–1558

    Article  Google Scholar 

  19. Lei B, Soon IY, Tan EL (2013) Robust svd-based audio watermarking scheme with differential evolution optimization. IEEE Trans Audio Speech Lang Process 21(11):2368–2378

    Article  Google Scholar 

  20. Dhar PK, Shimamura T (2014) Blind svd-based audio watermarking using entropy and log-polar transformation. J Inform Sec Appl 20(C):74–83

  21. Wu Q, Qu A, Huang D (2020) Robust and blind audio watermarking algorithm in dual domain for overcoming synchronization attacks. Math Probl Eng 2020:1–15

    Google Scholar 

  22. Zhao J, Zong T, Xiang Y, Gao L, Zhou W, Beliakov G (2021) Desynchronization attacks resilient watermarking method based on frequency singular value coefficient modification. IEEE/ACM Trans Audio Speech Lang Process 29:2282–2295. https://doi.org/10.1109/TASLP.2021.3092555

    Article  Google Scholar 

  23. Jiang W, Huang X, Quan Y (2019) Audio watermarking algorithm against synchronization attacks using global characteristics and adaptive frame division. Signal Process 162

  24. Benoraira A, Benmahammed K, Boucenna N (2015) Blind image watermarking technique based on differential embedding in dwt and dct domains. Eurasip J Adv Signal Process 2015(1):55

    Article  Google Scholar 

  25. Saadi S, Merrad A, Benziane A (2019) Novel secured scheme for blind audio/speech norm-space watermarking by arnold algorithm. Signal Process 154(JAN):74–86

  26. Bernardi G, Van Waterschoot T, Wouters J, Moonen M (2018) Subjective and objective sound-quality evaluation of adaptive feedback cancellation algorithms. IEEE/ACM Trans Audio Speech Lang Process 26(5):1–1

    Article  Google Scholar 

  27. Torcoli M, Kastner T, Herre J (2021) Objective measures of perceptual audio quality reviewed: An evaluation of their application domain dependence. arXiv e-prints

  28. Kabal P, et al (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, 1–89

  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 Processing

  30. Li J-F, Wang H-X, Wu T, Sun X-M, Qian Q (2018) Norm ratio-based audio watermarking scheme in dwt domain. Multimed Tools Appl 77(12):14481–14497

    Article  Google Scholar 

  31. Budiman G, Suksmono AB, Danudirdjo D (2020) Wavelet-based hybrid audio watermarking using statistical mean manipulation and spread spectrum. In: 2020 27th international conference on telecommunications (ICT), pp 1–5 . https://doi.org/10.1109/ICT49546.2020.9239581

  32. Dhar PK (2015) A blind audio watermarking method based on lifting wavelet transform and qr decomposition. In: 2014 8th international conference on electrical and computer engineering (ICECE)

Download references

Acknowledgements

This study was supported by the Sichuan Science and Technology program (Grant nos.2023NSFSC0470, 2022YFG0152, 2021YFQ0053), and the National Natural Science Foundation of China (NSFC) program (No.62171387, No.62202390).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xiangyi Liu or Canghong Shi.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflicts of interest that might influence the work in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Li, X., Shi, C. et al. A novel SVD-based adaptive robust audio watermarking algorithm. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18340-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18340-7

Keywords

Navigation