MSCE: Empowering Vessel Identity Anomaly Detection with Multimodal LLMs

With the rapid growth of global maritime traffic, some vessels manipulate their identities to evade regulatory oversight, posing significant challenges to maritime security. Existing anomaly detection methods, however, make limited use of navigational semantics, reducing their effectiveness in complex maritime environments. To address this, we propose MSCE—a Multimodal Semantic Cognition-Enhanced framework for vessel identity anomaly detection. MSCE leverages Large Language Models (LLMs) to analyze semantically enriched multimodal trajectories, capturing critical insights into vessel navigation preferences. Meanwhile, it employs multi-task representation learning to extract personalized spatio-temporal features closely linked to vessel identity. By integrating semantic and spatio-temporal features through a progressive cross-fusion mechanism, MSCE effectively accounts for both global navigational semantic consistency and local spatio-temporal similarity, thereby enhancing anomaly detection in dynamic maritime environments. Experiments on real-world maritime trajectory datasets show that MSCE surpasses existing methods, achieving improvements of 13.29% in accuracy, 10.53% in precision, 5.69% in recall, and 11.79% in F1-score.