Blockchain-Enabled Multi-Layered Product Authentication Against Counterfeit Products

Abstract

A strong, multi-layered verification system is required to protect international supply networks due to the spread of counterfeit goods. This study presents an Intelligent Multi-Layered Authentication Framework that blends deep learning-powered physical verification with blockchain-enabled immutability. The solution incorporates three levels of security: a QR code matrix linked to a decentralized blockchain offers a tamper-proof digital log for end-to-end traceability. At the same time, a hardware layer with NFC integration guarantees physical unclonability. Lastly, the system performs deep feature extraction using a Siamese Convolutional Neural Network (CNN) and a One-Shot Learning method. The inference engine finds minute differences in packaging textures, typography, and branding that are hard to spot with conventional scanning techniques by calculating the Euclidean Distance between a single master template and a live scan. This method ensures excellent scalability by doing away with the requirement for large training datasets for each unique product. Through Decision Fusion Logic, this multi-modal approach provides a "truthful" cross-verification of a product's digital and physical identities, greatly enhancing consumer trust and brand protection. In the end, this comprehensive strategy establishes a wise benchmark for the upcoming generation of anti-counterfeit systems.