Lagrangian Twin SVR Based Grayscale Image Watermarking Using LWT-QR Decomposition

  • Ashok Kumar YadavEmail author
  • Rajesh Mehta
  • Raj Kumar
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 4)


A novel approach of image watermarking using Lagrangian twin support vector regression (LTSVR) and combination of a variant of wavelet transform and QR decomposition for copyright protection application is proposed in this chapter. Firstly, host image decomposed into low-frequency subband (LL) and detail subbands by applying lifting wavelet transform (LWT). Secondly, the blocks of LL depend on the fuzzy entropy, and the selected regions of LL are transformed using QR factorization. Then, image dataset is formed using the elements of matrix R (called feature vector) of each selected block. This image dataset acts input to LTSVR to find the function approximation which defines the relationship between the input and target. The scrambled bits of the binary watermark are inserted into the predicted values obtained through the trained LTSVR upon comparing with the target value. The scrambled bits are obtained by applying Arnold transformation, which provide security to the proposed approach. Experimental results using various kinds of images and comparison of existing methods prove that the proposed approach is highly imperceptible and robust.


LTSVR LWT QR factorization Digital watermarking Fuzzy entropy 


  1. 1.
    Cox, I.J., Kilian, J., Leighton, F.T., Shamoon T.: Secure spread spectrum watermarking for multimedia. IEEE Trans. on Image Processing. 6, 1673–1687, 1997.Google Scholar
  2. 2.
    Moulin, P., Mincak, M.: A framework for evaluating the data-hiding capacity of image sources. IEEE Trans. on Image Processing. 11, 1029–1042, 2002.Google Scholar
  3. 3.
    Wen, X.B., Zhang, H.: A new watermarking approach based on probabilistic neural network in wavelet domain. Soft Computing, No. 13, pp. 355–360, 2009.Google Scholar
  4. 4.
    Peng, H., Wang, J.: Image watermarking method in multiwavelet domain based on support vector machines. The Journal of Systems and Software, No. 83, pp. 1470–1477, 2010.Google Scholar
  5. 5.
    Mehta, R., Rajpal, N., Vishwakarma, V. P.: Robust Image Watermarking Scheme in Lifting Wavelet Domain Using GA-LSVR Hybridization, International Journal of Machine Learning and Cybernetics, DOI:  10.1007/s13042-015-0329-6, 2015.
  6. 6.
    Mehta, R., Rajpal, N., Vishwakarma, V. P.: A robust and efficient image watermarking scheme based on Lagrangian SVR and lifting wavelet transform. International Journal of Machine Learning and Cybernetics, DOI:  10.1007/s13042-015-0329-z, 2015.
  7. 7.
    Shen, R.M., Fu, Y.G.: A novel image watermarking scheme based on support vector regression. The Journal of System and Software, No. 78, pp. 1–8, 2005.Google Scholar
  8. 8.
    Tang, G., Lio, X.: A neural network based blind watermarking scheme for digital images. Lecture Notes in Computer Science (LNCS), 3174, pp. 645–650, 2004.Google Scholar
  9. 9.
    Jing, Li., Liu, F.: Robust image watermarking scheme with general regression neural network and FCM algorithm. Lecture Notes in Computer Science (LNCS), 5226, pp. 243–250, 2008.Google Scholar
  10. 10.
    Balasundaram, S., Tanveer, M.: On Lagrangian twin support vector regression. Neural Computing and Applications, 22, 257–267, 2013.Google Scholar
  11. 11.
    Song, W., Jian-Jun, H., Zhao-Hong, Li., Liang, H.: Chaotic system and QR factorization based robust digital image watermarking algorithm. J. Cent. South Univ. Technology, No. 18, pp. 116–124, 2011.Google Scholar
  12. 12.
    Kumar, R., Das, R.R., Mishra, R.R., Dwivedi, R.: Fuzzy entropy based neuro-wavelet identifier-cum-quantifier for discrimination of gases/odors. IEEE sensors Journal. 11, 1548–1555, 2011.Google Scholar
  13. 13.
    Lei, B., Soon, I.Y., Zhou, F., Li Z., Lei, H.: A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition. Signal Processing, Vol. 92, No. 9, pp. 1985–2001, 2012.Google Scholar
  14. 14.
    Daubeches, I., Sweldens, W.: Factoring wavelets into lifting steps. Journal of Fourier Analysis and Applications, Vol. 4, No. 3, pp. 247–269, 1998.Google Scholar
  15. 15.
    Mangasarian, O.L., Musciant, D.R.: Lagrangian support vector machines. Journal of Machine Learning Research, Vol. 1, pp. 161–177, 2001.Google Scholar
  16. 16.
    Wu, L., Deng, W., Zhang, J., He, D.: Arnold transformation algorithm and anti Arnold transformation algorithm. In: Proc. of 1st International Conference on Information Science and Engineering (ICISE), pp. 1164–1167, 2009.Google Scholar
  17. 17.
    Balasundaram, S., Kapil: On Lagrangian support vector regression. Expert System with Applications, No. 37, pp. 8784–8792, 2010.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUIET, Maharishi Dayanand UniversityRohtakIndia
  2. 2.Department of Computer Science and EngineeringAmity School of Engineering and TechnologyNew DelhiIndia

Personalised recommendations