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Abstract. A robust copyright scheme for image protection based on visual secret sharing (VSS) and Bose-Chaudhuri-Hocquenghem (BCH) code techniques is proposed. This scheme not only maintains the quality of a host image without the change of any pixel value but also generates a meaningful ownership share to improve the management of image copyright. In addition, no codebook is required to store, and the watermark size is independent of the host image. The robustness of watermarking can be enhanced by BCH code. The proposed scheme contains ownership share construction and watermark extraction. In the first phase, an encoded watermark is generated by BCH code from a watermark. Next, an image feature is then extracted by the discrete wavelet transform decomposing from the host image. Finally, an ownership share can be generated by VSS technique from the image feature and the encoded watermark. In the second phase, a master share can be produced from a suspect image. By stacking the master and the ownership shares and using BCH code, an extracted watermark can be obtained. The experimental results show that the proposed scheme using the BCH(15,5) has better robust performance and practicability than existing schemes.

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Traditional watermarking techniques extract the watermark from a suspected image, allowing the copyright information regarding the image owner to be identified by the naked eye or by similarity estimation methods such as bit error rate and normalized correlation. However, this process should be more objective. In this paper, we implemented a model based on deep learning technology that can accurately identify the watermark copyright, known as WMNet. In the past, when establishing deep learning models, a large amount of training data needed to be collected. While constructing WMNet, we implemented a simulated process to generate a large number of distorted watermarks, and then collected them to form a training dataset. However, not all watermarks in the training dataset could properly provide copyright information. Therefore, according to the set restrictions, we divided the watermarks in the training dataset into two categories; consequently, WMNet could learn and identify the copyright information that the watermarks contained, so as to assist in the copyright verification process. Even if the retrieved watermark information was incomplete, the copyright information it contained could still be interpreted objectively and accurately. The results show that the method proposed by this study is relatively effective.