Apply the Counterpropagation Neural Network to Digital Image Copyright Authentication

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The rapid development of computer network and multimedia technology makes it easier to assess digital media. Since the problem of illegal reproduction and modification has become more serious than before, it is important to protect the intellectual property of digital media. 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. In this paper, we propose a novel method called full counterpropagation neural network (FCNN) for digital image watermarking, in which the watermark is embedded and extracted through specific FCNN. Different from the traditional methods, multiple cover images and a single watermark are embedded into the synapses of a FCNN instead of the cover image, i.e., multiple cover images and a single watermark image are memorized in the FCNN. 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.