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Home»Articles»An Improvement in Digital Image Watermarking Scheme Based on Singular Value Decomposition and Wavelet Transform

An Improvement in Digital Image Watermarking Scheme Based on Singular Value Decomposition and Wavelet Transform

Author : Manasha Saqib and Sameena Naaz
Volume 8 No.1 January-March 2019 pp 62-68

Abstract

An enormous growth of multimedia information in the internet has given rise to varied unauthorized use and modification. This authenticity issue is equally a limitation both in the defense data transmission and secured transmission. Digital watermarking is one of the legitimate solutions to the above problem since it makes possible validation and secure transmission of secret data. This paper presents a robust and secure digital image watermarking scheme that can be used for copyright protection. The scheme involves Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). The latest approach to wavelet transform is the Lifting Wavelet Transform and the significant transform technique for robust digital image watermarking is Singular Value Decomposition. The results are cross-validated by using inverse LWT and SVD. The digital signature mechanism is used to generate and embed a digital signature after the watermark is embedded, then the ownership is authenticated before extracting watermarks. In order to check the robustness of the method against various common image processing attacks like Mean, Median, Gaussian, Shear, Rotation and Crop, certain performance metrics such as peak to signal noise ratio (PSNR)and mean square error (MSE) is computed. To check similarity, normal cross-correlation (NCC) is used.

Keywords

Watermarking, Lifting Wavelet Transform, Singular Value Decomposition, Peak to Signal Noise Ratio, Mean Square Error, Normal Cross-Correlation

Full Text:

References

[1] V. Sthul and N. N. Khalsa, “Digital watermarking of an image using wavelet transform”, International Journal of Pure and Applied Research in Engineering and Technology (lJPRET), Vol. 8, No. pp.365-378, 2013.

[2] K.S. Rawat, S. Goyal, and R. Gupta, “Discrete wavelet based image watermarking: An idea leads to security”, Journal of Biometrics, Vol.1, No. pp.06-10, 2010.

[3] H.M. Yang, Y.Q. Liang, X.D. Wang, and S.J. Ji, “A DWT- based evaluation method of imperceptibility of watermark in watermarked color image”, International Conference on Wavelet Analysis and Pattern Recognition, 2007.

[4] J. Kaur, N. Singh, and C. Jain, “An improved image watermarking technique implementing 2-DWT and SVD”, IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2016.

[5] M. Radouane, R. Messoussi, R. Touahni, and T. Boujiha, “Robust method of digital image watermarking using SVD transform on DWT coefficients with optimal block”, International Conference on Multimedia Computing and Systems (ICMCS), 2014.

[6] N. S. Naik, N. Naveena, and K. Manikantan, “Robust digital image watermarking using DWT SVD approach”, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2015.

[7] T. Bhuyan, V. K. Srivastava, and F. Thakkar, “Shuffled SVD based robust and secure digital image watermarking”, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016.

[8] R. K. Sheth and V. V. Nath, “Secured digital image watermarking with discrete cosine transform and discrete wavelet transform method”, International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Spring), 2016.

[9] T. Pardhu and B. R. Perli, “Digital image watermarking in frequency domain”, International Conference on Communication and Signal Processing (ICCSP), 2016.

[10] A. Furqan and M. Kumar, “Study and Analysis of Robust DWT-SVD Domain Based Digital Image Watermarking Technique Using MATLAB”, IEEE International Conference on Computational Intelligence & Communication Technology, 2015.

[11] N. M. Makbol, B. E. Khoo, and T. H. Rassem, “Block-based discrete wavelet transform-singular value decomposition image watermarking scheme using human visual system characteristics”, IET Image Processing, Vol. 10, No. 1, pp. 34–52, 2016.

[12] Y. Dejun, Y. Rijing, L. Hongyan, and Z. Jiangchao, “A digital watermarking scheme based on singular value decomposition and discrete wavelet transform”, Proceedings of 2011 International Conference on Computer Science and Network Technology, 2011.

[13] V. Santhi and A. Thangavelu, “DWT-SVD Combined Full Band Robust Watermarking Technique for Color Images in YUV Color Space”, International Journal of Computer Theory and Engineering, pp. 424–429, 2009.

[14] H. Tao, L. Chongmin, J. Mohamad Zain and A. Abdalla, “Robust Image Watermarking Theories and Techniques: A Review”, Journal of Applied Research and Technology, Vol. 12, No. 1, pp. 122-138, 2014.

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An enormous growth of multimedia information in the internet has given rise to varied unauthorized use and modification. This authenticity issue is equally a limitation both in the defense data transmission and secured transmission. Digital watermarking is one of the legitimate solutions to the above problem since it makes possible validation and secure transmission of secret data. This paper presents a robust and secure digital image watermarking scheme that can be used for copyright protection. The scheme involves Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). The latest approach to wavelet transform is the Lifting Wavelet Transform and the significant transform technique for robust digital image watermarking is Singular Value Decomposition. The results are cross-validated by using inverse LWT and SVD. The digital signature mechanism is used to generate and embed a digital signature after the watermark is embedded, then the ownership is authenticated before extracting watermarks. In order to check the robustness of the method against various common image processing attacks like Mean, Median, Gaussian, Shear, Rotation and Crop, certain performance metrics such as peak to signal noise ratio (PSNR)and mean square error (MSE) is computed. To check similarity, normal cross-correlation (NCC) is used.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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