With the advancement of modern technologies, the digitization of metering data has significantly improved the efficiency and accuracy of data collection, analysis, and management. However, the growing prevalence of data tampering techniques has raised serious concerns regarding the trustworthiness and integrity of such data. To address this challenge, this study proposes an improved SM2 digital signature algorithm enhanced with high-precision time information to strengthen the reliability of metering data. The proposed algorithm incorporates high-precision timestamps into the signature generation and verification processes, while optimizing the structure of the signature algorithm—particularly the modular inversion operation—to reduce computational costs. Experimental results demonstrate that the improved algorithm not only significantly enhances signature generation efficiency but also improves temporal validity and security by leveraging high-precision time information. It effectively mitigates risks associated with random number dependency and replay attacks, offering a secure and efficient solution for trustworthy metering data verification.

With the development of the Internet of Things (IoT) and microelectronics technology, the methods used to tamper with fuel dispensers have become increasingly concealed, posing significant challenges to market supervision and law enforcement. This paper releases a tampering features dataset of assembled printed circuit boards (PCBs) from fuel dispensers, aiming to provide high-quality data support for automated, computer-vision-based illicit metering detection. The dataset encompasses multi-class tampering features derived from 189 high-resolution images of PCBs seized during real-world law enforcement, covering 5 mainstream brands. To eliminate perspective bias, rigorous lens distortion correction and four-point homography transformation preprocessing were conducted on the images. Additionally, six typical tampering features (e.g., the addition of tampered surface-mount resistors) were manually and precisely annotated, and then cross-checked and confirmed by domain experts. Furthermore, the dataset was benchmarked using multiple generations of You Only Look Once (YOLO) object detection models (Baseline Validation), which have been demonstrated to handle both large and small object detection in high-resolution images. The evaluation results, including confusion matrices and t-distributed Stochastic Neighbor Embedding (t-SNE) feature clustering diagrams, demonstrate the reliability and effectiveness of this dataset for training high-precision fraud detection models. This dataset is intended to support computer vision and anti-fraud research, promoting the automated development of fuel dispenser tampering detection.