Skip to main content
SpringerLink
Log in

A new scheme for watermark extraction using combined noise-induced resonance and support vector machine with PCA based feature reduction

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

Abstract

This manuscript presents a new scheme for binary watermark extraction using the combined application of noise-induced resonance (NIR) and support vector machine (SVM). The principal component analysis (PCA) is incorporated to minimize the dimension of the feature set obtained from the attacked watermarked image. The scheme utilizes lifting wavelet transform to decompose the original image into three levels, and blocks of low frequency sub-band coefficients are used for embedding purpose. Reference and signature information is embedded by quantizing the maximum and minimum coefficients of the corresponding block. Whereas, to extract the watermark, NIR-based tuning operation is performed. The transformed coefficients of the attacked watermarked image are tuned using iterative procedure of NIR in such a way that the transformed coefficients change their state from low signal-to-noise ratio (SNR) to maximum SNR or enhanced state. Finally, the tuned coefficients are fed into the machine i.e. SVM to classify as binary classes (0 or 1) which result in the corresponding watermark extraction. Experimental results of the proposed algorithm demonstrate noteworthy robustness against various signal processing attacks and remarkable improvements comparing with some of the recent techniques. Also, the scheme fulfills the requirements of image integrity in case of new strategic attack (i.e. print attack).

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Benzi R, Sutera A, Vulpiani A (1981) The mechanism of stochastic resonance. Int J Phys A: Math Gen 14(11):L453–L457

    MathSciNet  Google Scholar 

  2. Bhardwaj A, Verma VS, Jha RK (2018) Robust video watermarking using significant frame selection based on coefficient difference of lifting wavelet transform. Multimedia Tools and Applications 77(15):19659–19678

    Article  Google Scholar 

  3. Bulsara AR, Gammaitoni L (1996) Tuning in to noise. Int J Phys Today 49 (3):39–47

    Article  Google Scholar 

  4. Claypoole R, Baraniuk R, Nowak R (1998) Adaptive wavelet transforms via lifting. In: IEEE Int Conf Acoustics, Speech and Signal Process, vol 3. pp 1513–1516

  5. Cox IJ, Kilian J, Leighton F, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process 6(12):1673–1687

    Article  Google Scholar 

  6. Gammaitoni L, Hänggi P, Jung P, Marchesoni F (1998) Stochastic resonance. Rev Mod Phys 70(1):223–287

    Article  Google Scholar 

  7. Gard TC (1988) Introduction to stochastic differential equations. In: Marcel Dekker, New York

  8. Gu Q, Gao T (2013) A novel reversible robust watermarking algorithm based on chaotic system. Digital Signal Process 23(1):213–217

    Article  MathSciNet  Google Scholar 

  9. Jackson J (1991) A User’s Guide to Principal Components. Wiley, New York

    Book  MATH  Google Scholar 

  10. Jha RK, Chouhan R (2014) Dynamic stochastic resonance-based grayscale logo extraction in hybrid svd-dct domain. J Franklin Inst 351(5):2938–2965

    Article  MathSciNet  MATH  Google Scholar 

  11. Jolliffe I (1986) Principal Component Analysis. Springer, Berlin

    Book  MATH  Google Scholar 

  12. Jung P, Hänggi P (1991) Amplification of small signals via stochastic resonance. Int J Phys Rev A 44(12):8032–8042

    Article  Google Scholar 

  13. Lei B, Soon IY, 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):1985–2001

    Article  Google Scholar 

  14. Li X, Li J, Li B, Yang B (2013) High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction-error expansion. Signal Process 93 (1):198–205

    Article  Google Scholar 

  15. Luo H, Yu FX, Chen H, Huang ZL, Li H, Wang PH (2011) Reversible data hiding based on block median preservation. Inf Sci 181(2):308–328

    Article  Google Scholar 

  16. Luo L, Chen Z, Chen M, Zeng X, Xiong Z (2010) Reversible image watermarking using interpolation technique. IEEE Trans Inf Forensics Secur 5 (1):187–193

    Article  Google Scholar 

  17. Makbol NM, Khoo BE, Rassem TH, Loukhaoukha K (2017) A new reliable optimized image watermarking scheme based on the integer wavelet transform and singular value decomposition for copyright protection. Inf Sci 417:381–400

    Article  Google Scholar 

  18. Mardia K, Kent J, Bibby J (1979) Multivariate Analysis. Academic Press, New York

    MATH  Google Scholar 

  19. McNamara B, Wiesenfeld K (1989) Theory of stochastic resonance. Phys Rev A 39(9):4854–4869

    Article  Google Scholar 

  20. Peng H, Wang J, Wang W (2010) Image watermarking method in multiwavelet domain based on support vector machines. J Syst Softw 83(8):1470–1477

    Article  Google Scholar 

  21. Petitcolas FAP (1997) Weakness of existing watermarking schemes. http://www.petitcolas.net/fabien/watermarking/stirmark/index.html

  22. Risken H (1984) The fokker-planck equation. methods of solution and applications. In: Springer, Berlin

  23. Singh SP, Bhatnagar G (2018) A new robust watermarking system in integer dct domain. J Vis Commun Image Represent 53:86–101

    Article  Google Scholar 

  24. Sun S, Lei B (2008) On an aperiodic stochastic resonance signal processor and its application in digital watermarking. Signal Process 88(8):2085–2094

    Article  MATH  Google Scholar 

  25. Sweldens W (1996) The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl Comput Harmon Anal 3(2):186–200

    Article  MathSciNet  MATH  Google Scholar 

  26. Tsai HH, Sun DW (2007) Color image watermark extraction based on support vector machines. Inf Sci 177(2):550–569

    Article  Google Scholar 

  27. Verma VS, Jha RK (2015) Improved watermarking technique based on significant difference of lifting wavelet coefficients. Signal, Image and Video Process 9(6):1443–1450

    Article  Google Scholar 

  28. Verma VS, Jha RK (2015) An overview of robust digital image watermarking. IETE Tech Rev 32(6):479–496

    Article  Google Scholar 

  29. Verma VS, Jha RK, Ojha A (2015) Digital watermark extraction using support vector machine with principal component analysis based feature reduction. J Vis Commun Image Represent 31:75–85

    Article  Google Scholar 

  30. Verma VS, Jha RK, Ojha A (2015) Significant region based robust watermarking scheme in lifting wavelet transform domain. Expert Syst Appl 42(21):8184–8197

    Article  Google Scholar 

  31. Wang SH, Lin YP (2004) Wavelet tree quantization for copyright protection watermarking. IEEE Trans Image Process 13(2):154–165

    Article  MathSciNet  Google Scholar 

  32. Wang X, Li X, Yang B, Guo Z (2010) Efficient generalized integer transform for reversible watermarking. IEEE Signal Process Lett 17(6):567–570

    Article  Google Scholar 

  33. Wang XT, Chang CC, Nguyen TS, Li MC (2013) Reversible data hiding for high quality images exploiting interpolation and direction order mechanism. Digital Signal Process 23(2):569–577

    Article  MathSciNet  Google Scholar 

  34. Wang XY, Miao EN, Yang HY (2012) A new svm–based image watermarking using gaussianan–hermite moments. Appl Soft Comput 12(2):887–903

    Article  Google Scholar 

  35. Wang XY, Yang HY, Cui CY (2008) An svm-based robust digital image watermarking against desynchronization attacks. Signal Process 88(9):2193–2205

    Article  MATH  Google Scholar 

  36. Wu G, Qiu Z (2006) A novel watermarking scheme based on stochastic resonance. In: 8Th Int Conf Signal Process, vol 2. pp 1–4

  37. Yang HY, Wang XY, peng Wang C (2013) A robust digital watermarking algorithm in undecimated discrete wavelet transform domain. Comput Electr Eng 39 (3):893–906

    Article  Google Scholar 

  38. Ye Q, Huang R, He X, Zhang C (2003) A sr-based radon transform to extract weak lines from noise images. In: IEEE ICIP, vol 1. pp 849–52

Download references

Acknowledgment

Authors would like to express our gratitude to anonymous reviewers for their valuable suggestions and criticisms that have greatly improved the quality of this manuscript.

Author information

Authors and Affiliations

  1. Ajay Kumar Garg Engineering College, Ghaziabad, 201009, India

    Vivek Singh Verma

  2. Jaypee Institute of Information Technology, Noida, 201307, India

    Anuj Bhardwaj

  3. Indian Institute of Technology, Patna, 800013, India

    Rajib Kumar Jha

Authors
  1. Vivek Singh Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anuj Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajib Kumar Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anuj Bhardwaj.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Verma, V.S., Bhardwaj, A. & Jha, R.K. A new scheme for watermark extraction using combined noise-induced resonance and support vector machine with PCA based feature reduction. Multimed Tools Appl 78, 23203–23224 (2019). https://doi.org/10.1007/s11042-019-7599-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7599-z

Keywords

Search

Navigation