ABSTRACT Automatic liver segmentation is of great significance for computer‐aided treatment and surgery of liver diseases. However, respiratory motion often affects the liver, leading to image artifacts in liver magnetic resonance imaging (MRI) and increasing segmentation difficulty. To overcome this issue, we propose a global spatial structure‐aware attention model (GSA‐Net), a robust segmentation network developed to overcome the difficulties caused by respiratory motion. The GSA‐Net is an encoder‐decoder architecture, which extracts spatial structure information from images and identifies different objects using the minimum spanning tree algorithm. The network's encoder extracts multi‐scale image features with the help of an effective and lightweight channel attention module. The decoder then transforms these features bottom‐up using tree filter modules. Combined with the boundary detection module, the segmentation performance can be further improved. We evaluate the effectiveness of our method on two liver MRI benchmarks: one with respiratory artifacts and the other without. Numerical evaluations on different benchmarks demonstrate that GSA‐Net consistently outperforms previous state‐of‐the‐art models in terms of segmentation precision on our respiratory artifact dataset, and also achieves notable results on high‐quality datasets.

In a Collaborative Intelligent Transportation System (C-ITS), vehicles send collaborative awareness messages (CAMs) carrying information such as speed, position, and identity, to interact with other vehicles and to obtain transportation services. Traditionally, CAMs are broadcasted in plaintext over unsafe networks, which may leak sensitive information of the vehicle. On one hand, many existing works attempting to solve this problem focused on anonymous authentication, but ignored the confidentiality of CAMs. Conversely, some other attempts trying to keep the privacy of CAMs have backward security problems in key management or vehicle revocation. Therefore, in this paper, we present a practical privacy-preserving communication scheme for CAMs in C-ITS. To ensure the privacy of a vehicle's identity, our scheme mainly uses the re-randomizable PS signature method to realize anonymous authentication; and to meet backward security, we use the complete subtree method to realize both key management and vehicle revocation. In more details, our proposal fulfills more security and privacy requirements, such as correctness, anonymity, traceability, revocability, backward security and anti-illegal message dissemination, while also hiding the content of CAMs.

Cooperative intelligent transport systems (C-ITS) are advanced applications providing various services to enable a more secure, efficient, and accessible transportation system. When requesting services in C-ITS, privacy-preserving is crucial to avoid attempts in tracking vehicles who send continuous messages containing their authentication information. In this paper, we propose a revocable privacy-preserving authentication scheme through attribute-based credentials with complete subtree method. Differing from the existing anonymous credential works in C-ITS, our scheme supports the aggregation of attributes which facilitates the construction of a constant-size credential for multi-service. Not only maintaining the privacy-preserving of identity, the aggregate credential is also able to improve the efficiency of credential presentation. For illegal vehicles, our scheme revokes and disallows them to generate valid signatures via using complete subtree method in the non-revoked epoch. Moreover, we present analyses on aspects of security and performance, respectively. The former focuses on the security goals including privacy-preserving of identity, unlinkability, unforgeability, revocability and anti-replay attacks; while the latter shows that our scheme makes more practical sense for C-ITS.

In Collaborative Intelligent Transportation System (C-ITS), vehicles send collaborative awareness messages (CAMs) carrying information such as speed, position, and identity, to interact with other vehicles, and thus, obtain transportation services. However, CAMs are broadcasted in plaintext over the unsafe network, which causes the leakage of vehicle's sensitive information when maliciously intercepted. At present, there is less scheme for encryption and anonymous authentication CAMs that is suitable for the actual scenario. In this paper, we propose a revocable zone encryption scheme with anonymous authentication. When vehicles enter the zone, the zone manager first completes the anonymous authentication according to the vehicle-generated join request containing a self-delegated certificate. Then, vehicles encrypt CAMs with the session key by symmetric encryption and wrap the session key with the zone key for secure communication. Besides, we use the complete subtree method to satisfy the revocation of the vehicle and zone key management respectively. Finally, simulation experiments and analysis show that our scheme provides stronger privacy-preserving compared with proposals that only support anonymous authentication. Differing from the similar scheme that tries to encrypt CAMs, our scheme not only satisfies more security requirements but also our key management is more feasible.