An adaptive image watermarking scheme using non-separable wavelets and support vector regression

This paper presents an adaptive image watermarking scheme. Watermark bits are embedded adaptively into the non-separable wavelet domain based on the Human Visual System (HVS) model trained by Support Vector Regression (SVR). Unlike conventional separable wavelet filter banks that limit the ability in capturing directional information, non-separable wavelet filter banks contain the basis elements oriented at a variety of directions and different filter banks are able to capture different detail information. After removing the high frequency components, the low frequency subband used for watermark embedding is more robust against noise and other distortions. In addition, owing to the good generalization ability of the support vector machine, watermark embedding strength can be adjusted according to the HVS value. The superiority of non-separable wavelet transform (DNWT) in capturing image features combined with the good generalization ability of support vector regression provide us with a promising way to design a more robust watermarking algorithm featuring a better trade-off between the robustness and imperceptivity, the main duality of watermarking algorithms. Experimental results show that the DNWT watermarking scheme is robust to noising, JPEG compression, and cropping. In particular, it is more resistant to JPEG compression and noise than the discrete separable wavelet transform based scheme.