This paper presents a vision-based human interface system that enables a user to move a target object in a 3D CG world by moving his hand. The system can interpret hand motions both in a frame fixed in the world and a frame attached to the user. If the latter is chosen, the user can move the object forward by moving his hand forward even if he has changed his body position. In addition, the user does not have to keep in mind that his hand is in the camera field of view. The active camera system tracks the user to keep him in its field of view. Moreover, the system does not need any camera calibration. The key for the realization of the system with such features is vision algorithms based on the multiple view affine invariance theory. We demon-strate an experimental system as well as the vision algorithms. Human operation experiments show the usefulness of the system.

A method is presented to perform image segmentation by accumulatively observing apparent motion in a long image sequence of a dynamic scene. In each image in the sequence, locations are grouped into small patches of approximately uniform optical flow. To reduce the noise in computed flow vectors, a local image motion vector of each patch is computed by averaging flow vectors in the corresponding patches in several images. A segment contains patches belonging to the same 3-D plane in the scene. Initial segments are obtained in the image, and then an attempt to merge or split segments is iterated to update the segments. In order to remove inherent ambiguities in motion-based segmentation, temporal coherence between the local image motion of a patch and the apprent motion of every plane is investigated over long time. In each image, a patch is grouped into the segment of the plane whose apparent motion is temporally most coherent with the local image motion of the patch. When apparent motions of two planes are temporally incoherent, segments of the planes are retained as individual ones.

This paper describes a method for describing a three-dimensional (3-D) scene by integrating color and range data. Range data is obtained by a feature-based stereo method developed in our laboratory. A color image is segmented into uniform color regions. A plane is fitted to the range data inside a segmented region. Regions are classified into three types based on the range data. A certain types of regions are merged and the others remain unless the region type is modified. The region type is modified if the range data on a plane are selected by removing of the some range data. As a result, the scene is represented by planar surfaces with homogeneous colors. Experimental results for real scenes are shown.

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for data access control on cloud storage systems. In ABE, to revoke users' attributes, it is necessary to make them unable to decrypt ciphertexts. Some CP-ABE schemes for efficient attribute revocation have been proposed. However, they have not been given a formal security proof against a revoked user, that is, whether they satisfy forward secrecy has not been shown or they just do not achieve fine-grained access control of shared data. We propose an attribute revocable attribute-based encryption with the forward secrecy for fine-grained access control of shared data. The proposed scheme can use both “AND” and “OR” policy and is IND-CPA secure under the Decisional Parallel Bilinear Diffie-Hellman Exponent assumption in the standard model.

We propose a method of face identification under various illumination conditions. Because we use image based method for identification, the accurate position of the face is required. First, face features are detected, and the face region is determined using the features. Then, by registering the face region to the average face, the horizontal position of the face is adjusted. Finally, the size of the face region is adjusted based on the distance of two eyes determined from all input frames. If the sizes of images for all faces are normalized into one size, the face length feature is lost in the normalized face image. The face is classified into three categories according to the face length, and the subspace is generated in each category so that the face length feature is preserved. We demonstrate the effectiveness of the proposed method by experiments.

Internet of Things (IoT) has been widely applied in various fields. IoT data can also be put to cloud, but there are still concerns regarding security and privacy. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is attracted attention in cloud storage as a suitable encryption scheme for confidential data share and transmission. In CP-ABE, the secret key of a user is associated with a set of attributes; when attributes satisfy the access structure, the ciphertext is able to be decrypted. It is necessary that multiple authorities issue and manage secret keys independently. Authorities that generate the secret key can be regarded as managing the attributes of a user in CP-ABE. CP-ABE schemes that have multiple authorities have been proposed. The other hand, it should consider that a user's operation at the terminals is not necessary when a user drop an attribute and key is updated and the design of the communication system is a simple. In this paper, we propose CP-ABE scheme that have multiple key authorities and can revoke attribute immediately with no updating user's secret key for attribute revocation. In addition, the length of ciphertext is fixed. The proposed scheme is IND-CPA secure in DBDH assumption under the standard model. We compare the proposed scheme and the other CP-ABE schemes and show that the proposed scheme is more suitable for cloud storage.

The application of Intelligent Transport Systems (ITS) transmits data with road-to-vehicle communication (RVC) and inter-vehicle communication (IVC). Digital signature is essential to provide security for RVC and IVC. The public key certificate is used to verify that a public key belongs to an individual prover such as user or terminal. A certificate revocation list (CRL) is used for verifying validity of the public key certificate. A certificate authority (CA) publishes a CRL and distributes it to vehicles. CRL distribution traffic disturbs ITS application traffic because of sharing wireless channel between them. To distribute it on low bit rate will help to ease the disturbance. Although multiplex transmitting is effective in reliable communication, a duplication of received packets is waste of bandwidth as a consequence. This paper proposes a CRL distribution scheme based on random network coding which can reduce duplicate packets. The simulation results show that the number of duplicate packets of the proposed scheme is less than that of a simple error correction (EC)-based scheme and the proposed one can distribute CRL to more vehicles than EC-based ones.

Dedicated Short Range Communication (DSRC) is currently standardized as a leading technology for the implementation of Vehicular Networks. Non-safety application in DSRC is emerging beyond the initial safety application. However, it suffers from a typical issue of low data delivery ratio in urban environments, where static and moving obstacles block or attenuate the radio propagation, as well as other technical issues such as temporal-spatial restriction, capital cost for infrastructure deployments and limited radio coverage range. On the other hand, Content-Centric Networking (CCN) advocates ubiquitous in-network caching to enhance content distribution. The major characteristics of CCN are compatible with the requirements of vehicular networks so that CCN could be available by vehicular networks. In this paper, we propose a CCN-based vehicle-to-vehicle (V2V) communication scheme on the top of DSRC standard for content dissemination, while demonstrate its feasibility by analyzing the frame format of Beacon and WAVE service advertisement (WSA) messages of DSRC specifications. The simulation-based validations derived from our software platform with OMNeT++, Veins and SUMO in realistic traffic environments are supplied to evaluate the proposed scheme. We expect our research could provide references for future more substantial revision of DSRC standardization for CCN-based V2V communication.

In the context of Cyber-Physical System (CPS), analyzing the real world data accumulated in cyberspace would improve the efficiency and productivity of various social systems. Towards establishing data-driven society, it is desired to share data safely and smoothly among multiple services. In this paper, we propose a scheme that services authenticate users using information registered on a blockchain. We show that the proposed scheme has resistance to tampering and a spoofing attack.

System administrators and security officials of an organization need to deal with vulnerable IT assets, especially those with severe vulnerabilities, to minimize the risk of these vulnerabilities being exploited. The Common Vulnerability Scoring System (CVSS) can be used as a means to calculate the severity score of vulnerabilities, but it currently requires human operators to choose input values. A word-level Convolutional Neural Network (CNN) has been proposed to estimate the input parameters of CVSS and derive the severity score of vulnerability notes, but its accuracy needs to be improved further. In this paper, we propose a character-level CNN for estimating the severity scores. Experiments show that the proposed scheme outperforms conventional one in terms of accuracy and how errors occur.

Epidemic cyber incidents are caused by malicious websites using exploit kits. The exploit kit facilitate attackers to perform the drive-by download (DBD) attack. However, it is reported that malicious websites using an exploit kit have similarity in their website structure (WS)-trees. Hence, malicious website identification techniques leveraging WS-trees have been studied, where the WS-trees can be estimated from HTTP traffic data. Nevertheless, the defensive component of the exploit kit prevents us from capturing the WS-tree perfectly. This paper shows, hence, a new WS-tree construction procedure by using the fact that a DBD attack happens in a certain duration. This paper proposes, moreover, a new malicious website identification technique by clustering the WS-tree of the exploit kits. Experiment results assuming the D3M dataset verify that the proposed technique identifies exploit kits with a reasonable accuracy even when HTTP traffic from the malicious sites are partially lost.

The proliferation of coronavirus disease (COVID-19) has prompted changes in business models. To ensure a successful transition to non-face-to-face and electronic communication, the authenticity of data and the trustworthiness of communication partners are essential. Trust services provide a mechanism for preventing data falsification and spoofing. To develop a trust service, the characteristics of the service and the scope of its use need to be determined, and the relevant legal systems must be investigated. Preparing a document to meet trust service provider requirements may incur significant expenses. This study focuses on electronic signatures, proposes criteria for classification, classifies actual documents based on these criteria, and opens a discussion. A case study illustrates how trusted service providers search a document highlighting areas that require approval. The classification table in this paper may prove advantageous at the outset when business decisions are uncertain, and there is no clear starting point.

With a rapidly escalating number of sophisticated cyber-attacks, protecting Internet of Things (IoT) networks against unauthorized activity is a major concern. The detection of malicious attack traffic is thus crucial for IoT security to prevent unwanted traffic. However, existing traditional malicious traffic detection systems which relied on supervised machine learning approach need a considerable number of benign and malware traffic samples to train the machine learning models. Moreover, in the cases of zero-day attacks, only a few labeled traffic samples are accessible for analysis. To deal with this, we propose a few-shot malicious IoT traffic detection system with a prototypical graph neural network. The proposed approach does not require prior knowledge of network payload binaries or network traffic signatures. The model is trained on labeled traffic data and tested to evaluate its ability to detect new types of attacks when only a few labeled traffic samples are available. The proposed detection system first categorizes the network traffic as a bidirectional flow and visualizes the binary traffic flow as a color image. A neural network is then applied to the visualized traffic to extract important features. After that, using the proposed few-shot graph neural network approach, the model is trained on different few-shot tasks to generalize it to new unseen attacks. The proposed model is evaluated on a network traffic dataset consisting of benign traffic and traffic corresponding to six types of attacks. The results revealed that our proposed model achieved an F1 score of 0.91 and 0.94 in 5-shot and 10-shot classification, respectively, and outperformed the baseline models.

Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.

This paper first takes IoT as an example to provide the motivation for eliminating the single point of trust (SPOT) in a CA-based private PKI. It then describes a distributed public key certificate-issuing infrastructure that eliminates the SPOT and its limitation derived from generating signing keys. Finally, it proposes a method to address its limitation by all certificate issuers.

Integration of 21/2D sketches obtained at different observing stations into a consistent world (or object) representation is one of the central issues in computer vision and robotics. The resolution and accuracy of 21/2D sketches may be different from one view point to another, and inconsistent data between different observations may occur. This paper presents one approach to building a 3-D world representation from range image sequences for road scenes including moving objects. First, a range image is transformed into a height map representing the height information from the ground plane on which the observer is assumed to lie, then it is segmented into the ground plane and objects on it. In order to capture the resolution and accuracy of the range data and to reprsent the consistency of the height information between different height maps, we define a reliability of the height information for each grid on the height map. Using the reliability, the system finds the correspondences of both static and moving objects between different observations, and successively refines the height information and its reliability with newly acquired data, dealing with inconsistent data. We show the results applied to road scenes which are physically simulated by landscape toy models using a range finder based on the structured light.

We present a method for recognition of two-hand gestures. Two-hand gestures include fine-grain descriptions of hands under a complicated background, and have complex dynamic behaviors. Hence, assuming that two-hand gestures are an interacting process of two hands whose shapes and motions are described by switching linear dynamics, we propose a coupled switching linear dynamic model to capture interactions between both hands. The parameters of the model are learned via EM algorithm using approximate computations. Recognition is performed by selection of the model with maximum likelihood out of a few learned models during tracking. We confirmed the effectiveness of the proposed model in tracking and recognition of two-hand gestures through some experiments.