A novel robust scaling image watermarking scheme based on Gaussian Mixture Model

In this paper, we propose a novel scaling watermarking scheme in which the watermark is embedded in the low-frequency wavelet coefficients to achieve improved robustness. We demonstrate that these coefficients have significantly non-Gaussian statistics that are efficiently described by Gaussian Mixture Model (GMM). By modeling the coefficients using the GMM, we calculate the distribution of watermarked noisy coefficients analytically and we design a Maximum Likelihood (ML) watermark detector using channel side information. Also, we extend the proposed watermarking scheme to a blind version. Consequently, since the efficiency of the proposed method is dependent on the good selection of the scaling factor, we propose L-curve method to find the tradeoff between the imperceptibility and robustness of the watermarked data. Experimental results demonstrate the high efficiency of the proposed scheme and the performance improvement in utilizing the new strategy in comparison with the some recently proposed techniques.