A Multi-Modal Framework for Detecting Physical–Digital Container Identity Drift in Global Logistics
Received: 1 May 2026 | Revised: 20 May 2026 and 3 June 2026 | Accepted: 7 June 2026 | Online: 17 June 2026
Corresponding author: Manikandan Chandran
Abstract
This study addresses the problem of physical–digital container identity drift in global logistics, with the explicit objective of designing and evaluating a multi-modal, edge-deployable framework that detects sustained inconsistencies between a container's observable physical identifiers and its associated digital records in near real time. Modern container logistics relies on the continuous alignment of physical identifiers and digital records, yet this alignment frequently degrades due to repainting, reuse, environmental factors, and operational inconsistencies. Existing approaches rely on single-modality verification such as Optical Character Recognition (OCR) or document validation, which are insufficient in complex port environments. This paper proposes a cross-modal consistency framework deployed at the edge that integrates container imagery, OCR-extracted identifiers, Automatic Identification System (AIS)-derived vessel context, geospatial trajectories, and logistics metadata. A consistency-driven fusion engine evaluates pairwise agreement across modalities through six closed-form similarity functions, and a rule-augmented anomaly-scoring model identifies drift events. To address limitations of prior work, a hybrid evaluation dataset is constructed combining real-world container imagery with controlled perturbations representing visual, spatial, document, and temporal drift scenarios. Experiments executed on an NVIDIA Jetson Nano edge platform show that the proposed framework improves visual-drift F1 from 0.66 to 0.86, spatial-drift F1 from 0.59 to 0.84, document-drift F1 to 0.88, and matches the strongest learned baseline on temporal drift (F1 = 0.83), while sustaining 120 ms average end-to-end latency, 30 events/s throughput, and a 120 KB per-event bandwidth footprint. The proposed approach reframes container identity validation as a cross-modal consistency problem and provides a practical, deployable solution for enhancing supply-chain security and operational reliability.
Keywords:
container logistics, edge intelligence, multimodal data fusion, identity drift, OCR, AIS, supply chain securityReferences
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