Robust image watermarking technique using support vector regression for blind geometric distortion correction in lifting wavelet transform and singular value decomposition domain

Mohiul Islam; Rabul Hussain Laskar

doi:10.1117/1.JEI.27.5.053008

12 September 2018 Robust image watermarking technique using support vector regression for blind geometric distortion correction in lifting wavelet transform and singular value decomposition domain

Mohiul Islam, Rabul Hussain Laskar

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053008 (September 2018). https://doi.org/10.1117/1.JEI.27.5.053008

Abstract

This research work proposes a robust image watermarking technique for achieving robustness against desynchronization attacks using a geometric distortion correction (GDC) approach. The GDC is performed using support vector regression (SVR). The proposed algorithm uses the combined domain of lifting wavelet transform as well as singular value decomposition method. Incorporation of a support vector machine (SVM) has been done during watermark extraction to achieve enhanced robustness against diverse attack conditions. The present results show that the proposed technique achieves robustness against geometric and nongeometric attacks blindly by integrating SVR-based GDC technique with SVM-based binary watermark extraction. Again, the SVR-based blind GDC technique can estimate the geometric distortion of any arbitrary attacked image irrespective of the content present in it. Finally, comparative analysis with different state-of-the-art techniques suggests that the proposed technique provides improved performance in most of the cases.

Citation Download Citation

Mohiul Islam and Rabul Hussain Laskar "Robust image watermarking technique using support vector regression for blind geometric distortion correction in lifting wavelet transform and singular value decomposition domain," Journal of Electronic Imaging 27(5), 053008 (12 September 2018). https://doi.org/10.1117/1.JEI.27.5.053008

Received: 25 March 2018; Accepted: 6 August 2018; Published: 12 September 2018

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