The NextGen Quantum-Secure Edge AI-Blockchain System: Enhancing Supply Chain Trust with Vision Transformers

Abstract

Counterfeit products pose a critical threat to global supply chains, jeopardizing consumer safety, brand reputation, and economic stability. This paper introduces the NextGen Quantum-Secure Edge AI–Blockchain System, a scalable and resilient framework that integrates Vision Transformers (ViT), Federated Learning (FL), Blockchain, and Zero-Knowledge Proofs (ZKP) to achieve high-accuracy counterfeit detection and transparent product authentication. Leveraging multi-modal verification, combining RFID metadata validation, IoT-based anomaly detection, and advanced image analysis, the system ensures robust authentication while preserving data privacy. A hybrid on-chain/off-chain storage model optimizes data management and reduces blockchain congestion, while the novel PoA-X consensus mechanism enhances transaction throughput and minimizes latency. Post-quantum cryptographic primitives further safeguard against emerging quantum threats. Experimental evaluation demonstrates over 96% detection accuracy, stable counterfeit detection times of 355–395 ms, and an average throughput of 174 transactions per second, maintaining strong performance even under high traffic and adversarial conditions. By uniting adaptive AI, decentralized verification, and quantum-secure cryptography, the proposed framework delivers a future-proof, privacy-preserving, and highly efficient solution for counterfeit mitigation in global supply chains.