A Computational Framework for Dark-Vessel Screening: Integrating SAR–AIS Fusion, OSINT, and Social Network Analysis

Vessels disabling their Automatic Identification System (AIS) transponder evade AIS-based analytics. We present a framework for maritime dark-vessel screening—auditable candidate attributions for analyst review—coupling synthetic aperture radar (SAR) detection, AIS–SAR association, open-source intelligence (OSINT), and social network analysis (SNA). A Dynamic Kinematic Gate (DKG) grows its radius with speed and AIS staleness, up to 50 km, and demotes stale unmatched AIS to a low-confidence class. On 200 synthetic scenes, it raises true-match recall from 0.65 (capped static gate) to 0.88 and cuts spoofing alerts 4.1-fold via demotion; a fixed 10 km gate reaches 0.91 with fewer false confirmations, so the ceiling needs re-tuning. Re-benchmarked on five seeded SSDD folds with one evaluation code, YOLOv8s, YOLOv10s, and YOLOv11n reach a ship-class average precision ([email protected]) of 0.940–0.955, on par with the deployed detector SLA-Net (Faster R-CNN with mask-attention and transformer modules; 0.938–0.941), whose two modules bring no significant gain. Without retraining, SLA-Net fails on inshore scenes of two other datasets ([email protected] ≤0.19). On a 12-day operational corpus the static-gate pipeline logs 3190 confirmed matches and 1606 dark detections; OSINT registry documents agree with the AIS-declared name for 90.1% and the flag for 71.8% of 2000 sampled vessels (consistency, not accuracy); a real one-month SNA graph shows no distinct bridge vessel. Attribution remains an analyst-adjudicated judgment.