Vehicular platooning demands secure and trustworthy inter-vehicle communications to maintain platoon stability, traffic efficiency, and driving safety. Existing privacy-preserving authentication schemes protect message integrity over open channels but often overlook the credibility of message content, allowing authenticated yet misleading data. Reputation management frameworks address this by evaluating vehicle behavior, yet most employ periodic updates, delaying detection of sudden misbehavior. This paper proposes a reputation-based authentication mechanism (EPRU) that establishes trust between vehicles by evaluating vehicle behavior and historical records, thereby ensuring the reliability of message transmission between vehicles. Our EPRU incorporates a dynamic aggregation trigger mechanism that updates vehicle credit scores based on verified feedback collected in real time, thereby accurately reflecting behavioral changes. Furthermore, a combination of ElGamal encryption and homomorphic encryption is employed to safeguard vehicle identities and feedback data during transmission and processing. Formal security proofs and extensive analysis demonstrate resistance to forgery, replay, and modification attacks, ensuring the confidentiality, integrity, and authenticity of both messages and feedback data. Experimental results demonstrate that our EPRU reduces computation overhead by at least 3.05% and communicational cost by 37.5% compared with recent state-of-the-art schemes, highlighting its practicality and efficiency for real-time, privacy-aware vehicular platoon systems.

Digital histopathology image segmentation can facilitate computer-assisted cancer diagnostics. Given the difficulty of obtaining manual annotations, weak supervision is more suitable for the task than full supervision is. However, most weakly supervised models are not ideal for handling severe intra-class heterogeneity and inter-class homogeneity in histopathology images. Therefore, we propose a novel end-to-end weakly supervised learning framework named WESUP. With only sparse point annotations, it performs accurate segmentation and exhibits good generalizability. The training phase comprises two major parts, hierarchical feature representation and deep dynamic label propagation. The former uses superpixels to capture local details and global context from the convolutional feature maps obtained via transfer learning. The latter recognizes the manifold structure of the hierarchical features and identifies potential targets with the sparse annotations. Moreover, these two parts are trained jointly to improve the performance of the whole framework. To further boost test performance, pixel-wise inference is adopted for finer prediction. As demonstrated by experimental results, WESUP is able to largely resolve the confusion between histological foreground and background. It outperforms several state-of-the-art weakly supervised methods on a variety of histopathology datasets with minimal annotation efforts. Trained by very sparse point annotations, WESUP can even beat an advanced fully supervised segmentation network.