Perceptual hashing, which generates a message digest showing how humans perceive similarity in images, is suitable for ensuring the equivalence between a modified/edited image and the original. Conventional perceptual hashing is mainly utilized for similarity-based image retrieval and is not appropriate for image identification, which is required in security systems such as digital rights management. We previously developed a construction method for perceptual hashing in security systems that utilizes a convolutional neural network (CNN). In practical applications, multiple different images are published in various media forms (e.g., articles or books), so generating an identical message digest for each of these images simultaneously makes content management easier. Therefore, in this work we extend our earlier CNN-based perceptual hashing scheme so that it can generate an identical message digest for images in a group. This approach reduces the computational cost for fine-tuning CNN compared to generating a perceptual hash for each image in a group individually.

Perceptual hashing generates a message digest based on the image content of the human visual system, and differs from the cryptographic hash function that generates the hash value based on each bit of the image file. We apply perceptual hashing to digital watermarking to generate watermark information after each image modification/editing, and verify that modified/edited images and the original image are the same in copyright. To obtain a stable perceptual hash value robust to image modification/editing for digital watermarking, we previously developed a construction method for perceptual hashing using a convolutional neural network (CNN). This was necessary because the conventional perceptual hash algorithms are used for database retrieval, and the required characteristics are different from those used for digital watermarking. However, in this method we needed to fine-tune the CNN for each image used to calculate the perceptual hash value, which led to inefficiency. In order to make the calculation of the perceptual hash value more efficient, we propose a construction method for perceptual hashing based on CNN that does not require fine-tuning. In the proposed method, an image is input to the CNN and the perceptual hash value is calculated based on the response of the output layer of the trained CNN.

Digital watermarking technology is used extensively in the field of digital rights management. However, there are a few problems when it comes to making effective use of digital watermarking. First, for conventional digital watermarking, a digital image is used only as a carrier for embedded watermarking information, and as this information may be diverted to other images, the watermark information needs to be generated based on the original image. Second, after the original image is modified/edited, the watermark information needs to prove that it is from the original image. Third, multiple digital watermarks need to be stored and managed without depending on trusted third parties. In an earlier work, we proposed a digital rights management system based on digital watermarking, blockchain, and perceptual hashing to resolve these issues. However, because we used conventional perceptual hashing, we could not draw sufficient conclusions about the first and second problems. In order to obtain a stable digest message of an image for digital watermarking, we here propose a new construction method for perceptual hashing using a convolutional neural network (CNN). In the proposed method, we first construct a machine-learned CNN for accepting an image that we want to take the perceptual hash value. The perceptual hash value is the cryptographic hash value of the weights that make up the CNN. We then verify that the reconstructed CNN can guarantee the hash value used when obtaining the hash value, and confirm that the image to be verified is accepted and is the perceptual hash value of this image.

In the past, the improvement of digital copyright protection system based on digital watermarking mainly focused on algorithms, while generation and storage of the watermark information was ignored. In this paper, a new design scheme of copyright management system based on digital watermarking and its information, such as blockchain, is proposed, which combines digital watermarking, blockchain, perceptual hash function, Quick Response(QR) code, and InterPlanetary File System(IPFS). Among them, blockchain is used to securely store watermark information and provide timestamp authentication for multiple watermarks (multiple copyrights) to confirm the creation order. Perceptual hash function is used to generate hash value based on the structure information of images, that watermark information can be confirmed without the original image. QR code is used to generate QR code images containing image hash and copyright information as watermark images to improve robustness and capacity of digital watermarking; IPFS is used to store and distribute watermarked images without a centralized server. This scheme can enhance the effectiveness of digital watermarking technology in the field of copyright protection. In this way, use P2P network to integrate and complete copyright management and distribution of copyrighted works without requiring a trusted third party. Nodes rely on cryptography to confirm the identity of each other and ensure the security of information. It can reduce information leakage, data destruction and other risks caused by collapse of the centralized system in the past. This improves the security and transparency of information, and speeds up the distribution of copyrighted works to facilitate circulation in the network. This scheme can also improve copyright protection of multiple creations. Combine blockchain and multiple digital watermarks to record copyright information of every copyright owner in the authoring process and fully prove this information. In order to protect the legitimate rights and interests of each copyright owner.