Hiding a logo watermark into the multiwavelet domain using neural networks

This paper proposes a novel watermarking scheme for an image, in which a logo watermark is embedded into the multiwavelet domain of the image using neural networks. The multiwavelet domain provides us with a multiresolution representation of the image like the scalar wavelet case. However, there are four subblocks in the coarsest level of the multiwavelet domain, where there is only one in that of the scalar wavelet domain, and also there is a great similarity among these subblocks. According to these characteristics of the multiwavelet domain, we embed a bit of the watermark by adjusting the polarity between the coefficient in one subblock and the mean value of the corresponding coefficients in other three subblocks. Furthermore, we use a back-propagation neural network (BPN) to learn the characteristics of relationship between the watermark and the watermarked image. Due to the learning and adaptive capabilities of the BPN, the false recovery of the watermark can be greatly reduced by the trained BPN. Experimental results show that the proposed method has good imperceptibility and high robustness to common image processing operators.