An Adaptive Watermark Scheme Based on Contourlet Transform

Based on the convergence and stability of the new mean shift fast algorithm, an adaptive watermark algorithm in contourlet domain based on mean shift texture features clustering is proposed in this paper. Through the texture recognition method based on gray co-occurrence matrix, watermark is embedded into the coefficients in contourlet domain, which makes the capability of the watermark more covert, anti-noise attack and robust. During the clustering, three texture features including energy, entropy and contrast were selected for mean shift fast clustering algorithm. Strong regional textures of host images are extracted directly, accurately and efficiently, and the embedding intensity can be gain automatically then. The experiment shows that this algorithm has the strong robustness to Gauss low pass filter, Wiener filter, median filtering, Salt and pepper noise, Gaussian noise, JPEG compression, shear attack etc. It is a blind detection, and adapt to various of images.