A SVD Feature based Watermarking Algorithm for Gray-level Image Watermark

In this paper, a novel gray-level image watermarking algorithm based on Singular Value Decomposition (SVD) feature and neural network is presented for copyright protection. Firstly, a new method which encodes the gray-level secret information into bit string is used. It uses the eigenvalues of SVD to extract the most valuable features of the watermark image and reduces lots of redundant information. Then based on human visual system’s characteristic, the SVV (Singular Value and Variance) method is adopted to analyze the host image and choose the regions suitable for watermark embedding. Finally, the back propagation neural network is utilized for watermark recovery. Relying on the information contained in the extracted eigenvalues, the neural network is trained to recognize the original watermark. The application of the proposed scheme makes the gray-level watermark capable of transparent hiding. Results demonstrate that our method is robust to the intentional attacks and performs better than traditional methods.