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Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization

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Abstract

This paper presents an imperceptible, robust, secure and efficient image watermarking scheme in lifting wavelet domain using combination of genetic algorithm (GA) and Lagrangian support vector regression (LSVR). First, four subbands low–low (LL), low–high (LH), high–low (HL) and high–high (HH) are obtained by decomposing the host image from spatial domain to frequency domain using one level lifting wavelet transform. Second, the approximate image (LL subband) is divided into non overlapping blocks and the selected blocks based on the fuzzy entropy are used to embed the binary watermark. Third, based on the correlation property of each transformed selected block, significant lifting wavelet coefficient act as target to LSVR and its neighboring coefficients (called feature vector) are set as input to LSVR to find optimal regression function. This optimal regression function is used to embed and extract the scrambled watermark. In the proposed scheme, GA is used to solve the problem of optimal watermark embedding strength, based on the noise sensitivity of each selected block, in order to increase the imperceptibility of the watermark. Due to the good learning capability and high generalization property of LSVR against noisy datasets, high degree of robustness is achieved and is well suited for copyright protection applications. Experimental results on standard and real world images show that proposed scheme not only efficient in terms of computational cost and memory requirement but also achieve good imperceptibility and robustness against geometric and non geometric attacks like JPEG compression, median filtering, average filtering, addition of noise, sharpening, scaling, cropping and rotation compared with the state-of-art techniques.

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References

  1. Agarwal C, Mishra A, Sharma A (2013) Gray-scale image watermarking using GA-BPN hybrid network. J Vis Commun Image R. 24(7):1135–1146

    Article  Google Scholar 

  2. Ali M, Ahn CW, Pant M (2014) A robust image watermarking technique using SVD and differential evolution in DCT domain. Optik 125(1):428–434

    Article  Google Scholar 

  3. Aslantas V (2008) A singular value decomposition based image watermarking using genetic algorithm. Int J Electron Commun 62(5):386–394

    Article  Google Scholar 

  4. Avci Engin, Avci Derya (2009) An expert system based on fuzzy entropy for automatic threshold selection in image processing. Expert Syst Appl 36:3077–3085

    Article  Google Scholar 

  5. Balasundaram S, Kapil (2010) On Lagrangian support vector regression. Expert Syst Appl 37(12):8784–8792

    Article  Google Scholar 

  6. Balasundaram S, Tanveer M (2013) On Lagrangian twin support vector regression. Neural Comput Appl 22:S257–S267

    Article  Google Scholar 

  7. Bender W, Gruhl D, Morimoto N, Lu A (1996) Techniques for data hiding. IBM Syst J. 25:313–335

    Article  Google Scholar 

  8. Chen GY, Kegl B (2010) Invariant pattern recognition using contourlets and AdaBoost. Pattern Recogn 43(3):579–583

    Article  MATH  Google Scholar 

  9. Chen HY, Zhu YS (2012) A robust watermarking algorithm based on QR factorization and DCT using quantization index modulation technique. J Zhejiang Univ-Sci C (Comput & Electron) 13(8):573–584

    Article  Google Scholar 

  10. Chu W-C (2003) DCT based image watermarking using sub-sampling. IEEE Trans Multimed 5(1):34–38

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Daubeches I, Sweldens W (1998) Factoring wavelet transform into lifting steps. J Fourier Anal Appl 4(3):247–269

    Article  MathSciNet  MATH  Google Scholar 

  13. Ding S, Yu J, Huang B-QH (2014) An overview on twin support vector machines. Artif Intell Rev 42:245–252

    Article  Google Scholar 

  14. Fan W, Chen J, Zhen J (2005) SPIHT algorithm based on fast lifting wavelet transform in image compression. In: Hao Y et al (eds) CIS 2005, Part II, LNAI, vol 3802., pp 838–844

    Google Scholar 

  15. Feng G, Qian Z, Dai N (2012) Reversible watermarking via extreme learning machine prediction. Neurocomputing 82:62–68

    Article  Google Scholar 

  16. Gen M, Cheng R (1997) Genetic algorithms and engineering design. Wiley, New York

    Google Scholar 

  17. Halder R, Pal S, Cortesi A (2010) Watermarking techniques for relational databases: survey, classifications and comparison. J Univers Comput Sci 16(21):3164–3190

    Google Scholar 

  18. Huang H-C, Chu C-M, Pan J-S (2009) The optimized copyright protection scheme with genetic watermarking. Soft Comput 13:333–343

    Article  Google Scholar 

  19. Khemchandani R, Karpatne A, Chandra S (2013) Twin support vector regression for the simultaneous learning of a function and its derivatives. Int J Mach Learn Cybernet 4(1):51–63

    Article  Google Scholar 

  20. Klir GJ, Yuan B (1995) Fuzzy sets and Fuzzy logic: theory and applications. Prentice Hall, New Jersey

    MATH  Google Scholar 

  21. 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. Sig Process 92(9):1985–2001

    Article  Google Scholar 

  22. Lin WH, Wang YR, Hong SJ, Kao TW, Pan Y (2009) A blind watermarking method using maximum wavelet coefficient quantization. Expert Syst Appl 36(9):11509–11516

    Article  Google Scholar 

  23. Loukhaoukha K, Chouinard J-Y, Taieb M-H (2010) Multi-Objective genetic algorithm optimization for image watermarking based on singular value decomposition and Lifting wavelet transform. Lect Notes Comput Sci (LNCS) 6134:394–403

    Article  Google Scholar 

  24. Maity SP, Maity S, Sil J, Delpha C (2013) Collusion resilient spread spectrum watermarking in M-band wavelet using GA-fuzzy hybridization. J Syst Softw 86(1):47–59

    Article  Google Scholar 

  25. Mangasarian OL, Musciant DR (2001) Lagrangian support vector machines. J Mach Learn Res 1:161–177

    MathSciNet  MATH  Google Scholar 

  26. Mehta R, Mishra A, Singh R, Rajpal N (2010) Digital watermarking in DCT domain using finite newton support vector regression. In: IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing IIH-MSP (2010), pp 123–126

  27. Mehta R, Rajpal N, Vishwakarma VP (2014) Subband discrete cosine transform based grayscale image watermarking using general regression neural network. Int J Signal Imaging Syst. ISSN online: 1748-0701 (forthcoming articles)

  28. Nikolaidis N, Pitas I (1998) Robust image watermarking in the spatial domain. Sig Process 66(3):385–403

    Article  MATH  Google Scholar 

  29. Pablo RP, Juan CR, Rosiles JG (2014) A nonlinear least squares quasi-Newton strategy for LP-SVR hyper-parameters selection. Int J Mach Learn Cybernet 5(4):579–597

    Article  Google Scholar 

  30. Peng J, Zhang D (2009) Image encryption and chaotic cellular neural network. In: Tsai JJP, Yu PS (eds) Machine learning in cyber trust. Springer, New York, pp 183–213

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

    Article  Google Scholar 

  32. Piao CR, Beack S, Woo D-M, Han S-S (2006) A blind watermarking algorithm based on HVS and RBF neural network for digital image. Lect Notes Comput Sci (LNCS) 4221:493–496

    Article  Google Scholar 

  33. Qi Z, Tian Y, Shi Y (2012) Laplacian twin support vector machine for semi-supervised classification. Neural Netw 35:46–53

    Article  MATH  Google Scholar 

  34. Rawat S, Raman B (2012) A blind watermarking algorithm based on fractional Fourier transform and visual cryptography. Sig Process 92(6):1480–1491

    Article  Google Scholar 

  35. Sharmila T, Kumar K (2012) Efficient analysis of hybrid direction lifting technique for satellite image denoising. Signal Image Video Process. Springer-Verlag London Limited, ISSN: (2012): 863–1703

  36. Shen R, Fu Y, Lu H (2005) A novel image watermarking scheme based on support vector regression. J Syst Softw 78(1):1–8

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  38. Tao H, Zain JM, Ahmed N, Hingwu Q (2010) An implementation of digital image watermarking based on particle swarm optimization. CCIS 87:314–320

    Google Scholar 

  39. Verma VS, Jha RK (2014) Improved watermarking technique based on significant difference of lifting wavelet coefficients. SIViP. doi:10.1007/s11760-013-0603-6

    Google Scholar 

  40. Wang J, Peng H, Shi P (2011) An optimal image watermarking approach based on multi-objective genetic algorithm. Inf Sci 181(24):5501–5514

    Article  Google Scholar 

  41. Wang Y, Wong KW, Liao X, Chen G (2011) A new chaos based fast image encryption algorithm. Appl Soft Comput 11:514–522

    Article  Google Scholar 

  42. Wu L, deng W, Zhang J, He D (2009) Arnold transformation algorithm and anti Arnold transformation algorithm. In: Proceedings of 1st International Conference on Information Science and Engineering (ICISE) 2009. pp 1164–1167

  43. Zheng S (2013) A fast algorithm for training support vector regression via smoothed primal function minimization. Int J Mach Learn Cybernet. doi:10.1007/s13042-013-0200-6

    Google Scholar 

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Mehta, R., Rajpal, N. & Vishwakarma, V.P. Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization. Int. J. Mach. Learn. & Cyber. 9, 145–161 (2018). https://doi.org/10.1007/s13042-015-0329-6

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  • DOI: https://doi.org/10.1007/s13042-015-0329-6

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