An Adaptive Image Watermarking Method Combining SVD and Wang-Landau Sampling in DWT Domain

Baowei Wang and Peng Zhao
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Baowei Wang: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Peng Zhao: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

Mathematics, 2020, vol. 8, issue 5, 1-20

Abstract: To keep a better trade-off between robustness and imperceptibility is difficult for traditional digital watermarks. Therefore, an adaptive image watermarking method combining singular value decomposition (SVD) and the Wang–Landau (WL) sampling method is proposed to solve the problem. In this method, the third-level approximate sub-band obtained by applying the three-level wavelet transform is decomposed by SVD to obtain the principal component, which is firstly selected as the embedded position. Then, the information is finally embedded into the host image by the scaling factor. The Wang–Landau sampling method is devoted to finding the best embedding coefficient through the proposed objective evaluation function, which is a global optimization algorithm. The embedding strength is adaptively adjusted by utilizing the historical experience, which overcomes the problem of falling into local optimization easily in many traditional optimization algorithms. To affirm the reliability of the proposed method, several image processing attacks are applied and the experimental results are given in detail. Compared with other existing related watermarking techniques based on both qualitative and quantitative evaluation parameters, such as peak signal to noise ratio (PSNR) and normalized cross-correlation (NC), this method has been proven to achieve a trade-off between robustness and invisibility.

Keywords: digital image watermarking; Wang–Landau (WL) sampling method; historical experience; discrete wave transformation (DWT); singular value decomposition (SVD) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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