The application of a full counterpropagation neural network to image watermarking

Abstract

Digital watermarks are an important technique for protection and identification that allows authentic watermarks to be hidden in multimedia such as image, audio, and video. Watermarking has been developed to protect digital media from being illegally reproduced and modified. Embedding and extracting watermark used to require complex procedures. These include randomizing the watermark, choosing positions to embed and extract it, embedding the randomized watermark into the specific positions, and extracting it from the specific positions. In this paper, we propose a novel method called full counter-propagation neural network (FCNN) for digital image watermarking, in which the watermark is embedded and extracted through specific FCNN. Different from the traditional methods, the watermark is embedded in the synapses of FCNN instead of the cover image. Therefore, the watermarked image is almost the same as the original cover image. In addition, most of the attacks could not degrade the quality of the extracted watermark image. The experimental results show that the proposed method is able to achieve robustness, imperceptibility and authenticity in watermarking.