Trust Measurement of Visual Data Based on Multigranularity Belief Fusion for UAV Perception System

Unmanned aerial vehicle (UAV) visual perception plays a pivotal role in the realm of intelligent big data applications such as smart transportation, urban planning, and precision agriculture. Ensuring the integrity and reliability of data collected by UAVs is essential to prevent potential misinformation and tampering in critical data acquisition tasks. However, conventional single assessment and decision-making mechanisms have demonstrated limitations, leaving systems vulnerable to security threats in real-world scenarios. In this study, we propose an innovative trust measurement strategy for UAV visual data based on multigranularity belief fusion (TM-MGBF), aimed at bolstering the reliability of UAV visual data and mitigating the risks posed by malicious attacks. The integration of multigranularity trust (MGT) measurement offers enhanced adaptability and accuracy, offering a promising avenue to address the aforementioned challenges. Specifically, our approach involves devising distinct methodologies for assessing trust across diverse data sources to ascertain their trustworthiness. Furthermore, we introduce an improved fusion strategy, achieving more rational trust measurement by weighting the importance assessment of trust evidence. Experimental results validate the efficacy of the proposed trust model based on multigranularity belief fusion (MGBF), showcasing its superior performance in the evaluation of visual perception data and the fortification of UAV perception systems’ security posture.