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.

This study proposes a user‐friendly XOR‐based visual secret sharing scheme using random grids. In some visual secret sharing schemes, problems such as pixel expansion or noisy and meaningless shares may be encountered. In the proposed scheme, different regions in the shared image will have different brightness levels depending on the cover image's pixel values so that the generated shares contain information regarding the cover image and are not just a meaningless noisy image. This can overcome the difficulty in recovering the secret image in cases wherein the shares were mixed up by mistake. Furthermore, the dimension of the share is the same as the secret image and there are no pixel expansion problems. The size of the recovered secret image is consistent with the size of the original secret image, and there are no changes in scaling. Lastly, although one of the generated share is a complementary cover image, the image quality of the recovered secret image is still favourable when the secret image is recovered. The experimental and analysis results demonstrate that the proposed scheme is more effective than other schemes.