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SVM correction based geometrically invariant digital watermarking algorithm

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Abstract

Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust image watermarking scheme against geometric distortions. Based on the Support Vector Machine (SVM) geometric distortions correction, we propose a new image watermarking algorithm with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the significant bitplane image is extracted from original host, and DWT is performed on the significant bitplane image. Then, the corresponding low-pass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by adaptively modulating the selected wavelet coefficients in small blocks. The main steps of digital watermark detecting procedure include: (1) the significant bitplane image is extracted from test image, and some low-order pseudo-Zernike moments of the significant bitplane image are computed, which are regarded as the effective feature vectors; (2) the appropriate kernel function is selected for training, and a SVM training model can be obtained; (3) the test image is corrected with the well trained SVM model; (4) the digital watermark is extracted from the corrected test image. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc., but also robust against the geometrical distortions.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61272416, 60873222, & 60773031.

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Correspondence to Xiang-yang Wang or Hong-ying Yang.

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Wang, Xy., Wang, Cp., Wang, Al. et al. SVM correction based geometrically invariant digital watermarking algorithm. Multimed Tools Appl 72, 1933–1960 (2014). https://doi.org/10.1007/s11042-013-1483-z

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  • DOI: https://doi.org/10.1007/s11042-013-1483-z

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