Analysis of Digital Watermarking using full Counter Propagation Neural Networks and Hopfield Model

Digital Watermarking offers techniques to hide watermarks into digital content to protect it from illegal copy or reproduction. Existing techniques based on spatial and frequency domain suffer from the problems of low Peak Signal to Noise Ratio (PSNR) of watermark and image quality degradation in varying degree. In earlier papers, the author proposed only the watermark was embedded and extracted through specific fcnn technique. In this paper, we propose Hopfield model and full counter propagation neural network (fcnn) techniques to overcome the remedies such as peak signal to noise ratio (psnr) and to maintain the quality of the image. We also calculate the psnr and normal correlation by adding the white Gaussian noise.