Image watermarking is an important tool for copyright management, and various watermarking methods have been proposed. Tensor decomposition, which has attracted much attention in image processing, has been applied as a watermarking method, however, no method has been proposed for color cover images with grayscale watermarks. Therefore, we propose a color image watermarking method that involves using a grayscale watermark. We evaluated the attack resistance of the proposed method through experiments.

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.

Leakage of sensitive information concerned with database systems, such as social networking services and online storages, has occurred. One of the causes of such leakage is a covert channel, which is a path of information leakage caused by inconsistencies in access rights on databases. Another cause is an inference attack in which secret information is inferred from the fragments of open data. We previously proposed a hypergraph-based model used to prevent the possibility of information leaks by inference but considered only reading operations. However, writing operations also have to be considered. We also previously proposed covert-channel analysis models with inference rules and reading and writing operations. Management mechanisms for access histories to objects by users are required to analysis the satisfaction of the inference rules. If the accesses histories are altered, information leakage by an inference attack will occur. In this study, we used a blockchain to manage access logs to solve the above problems. A traditional covert channel is a static analysis based on the assumption that the access rights of access control lists do not change. Covert channels exist under certain conditions even if no covert channel is detected from static analysis. We define the authorization quad, which describes the relation among the subject as a user, object as a file, access rights, and timing of transition, to consider the transitions of access rights to analyze a covert channel. We also propose a management scheme for covert-channel analysis with a blockchain considering inference attacks and transitions of access rights.

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.

Various pieces of personal information are correlated to the My Number program, which establishes national identification numbers that are unique to each citizen and resident of Japan. Hence, its protection is paramount. However, whereas the aim of current security measures is to prevent leaks directly, we must consider the possibility of non-secret information being used to indirectly leak secret information by inference. We studied a hypergraph-based model that is used to prevent the possibility of information leaks by inference. Only reading operations are considered in our previous works. However, writing operations have to be considered. We propose covert channel analysis models with inference rules and reading and writing operations. In addition, management mechanisms for the history of accesses to objects by users are required to analysis the satisfaction of the inference rules. If the history of accesses are altered, the information leakages by the inference attack are occurred. In our research, a block chain is used to manage the access log to solve problems mentioned above. Furthermore, we propose a dynamic access control model with access log managed by the blockchain and the inference path detection model.

The long reach passive optical network (LR-PON) is a promising scheme for access networks that cover large areas. For designing an access network, an exact solution of the network performance (e.g., mean packet delay) is useful. Since long propagation delay causes unused time slots (idle time), and its effect on performance degradation is difficult to analyze, existing analysis of the LR-PON is done only for the no-idle time situation with modified optical line terminal (OLT) and each optical network unit (ONU). In this paper, we propose an exact solution of the mean packet delay for LR-PON with idle time. Our analytical solution is derived by a non-trivial extension of the existing work for the short range PON. We confirm a good match between the mean packet delay derived by the simulation and our analytical solution.

People today exchange a great variety of information over the Internet. Much like the world at large, people bring a wide range of values to their interaction with each other as they communicate over the Internet. While the Internet has certainly facilitated social interaction, security measures have become absolutely essential to ensure private information is not compromised or tampering with. These security-related problems are also related to human values, but it is not easy to represent complex human values by conventional agent-based simulation. In this paper, we propose a model of information propagation in social networks using particle swarm optimization (PSO), a type of swarm intelligence algorithm, to simulate how different values affect human interaction. Simulations based on the model reveal that effects on propagation of information are quite different between environments where information is exchanged among agents with the same values and environments where information is exchanged among agents with different values.

At the present, some automatic indexing techniques are desired in the field of image retrieving. The cost of retrieving will be reduced and indexing is done uniformly using an automatic indexing. In this paper, we study a method of automatic recognition of the motion in moving images for the purpose of the image retrieving we describe a method of the image retrieving using the moving object and the motion.

In conventional digital signature techniques, secret information, which is utilized for authentication, is disclosed to the verifier. A new digital signature system for image data is proposed. This system can be used to assert the copyright of image data. In this system, a graph generated from an image which must has a signature and an isomorphic graph is concealed in this image. The ZKIP (zero knowledge interactive proof) for the graph isomorphism is applied to assert the copyright of this image. Consequently the secret information is not disclosed during the authentication process.

A method for automated 2D human body description (stick figure) based on segmentation is described. Segmentation is based on contrast, motion, and knowledge of body structure. The human body (upper part), segmented into 6 parts, is represented by an adjacency graph. The regions assigned to each part are modeled as a Markov random field (MRF) on the graph, and the body part detection problem is then formulated as a maximum a posteriori (MAP) estimation. In addition, an algorithm for generating stick figures without excessive computation is proposed.