Blockchain-Enabled Product Tokenization and CNN-Based Counterfeit Detection for Secure Supply Chain Verification
DOI:
https://doi.org/10.70162/mijarcse/2025/v11/i1/v11i105Keywords:
Counterfeit Detection, Blockchain, Smart Contracts, Product Tokenization, Convolutional Neural Networks, Supply Chain Verification.Abstract
The global surge in counterfeit products poses a critical threat to consumer safety, brand reputation, and economic stability. Traditional anti-counterfeiting methods, including physical labels and centralized verification systems, have proven increasingly vulnerable to tampering, replication, and limited traceability. This study aims to design and implement a robust, end-to-end system that combines machine learning-based product classification with blockchain-enabled authenticity verification through smart contract-based tokenization. The proposed approach integrates a lightweight Convolutional Neural Network (CNN) trained on a publicly available fake product detection dataset containing 2,364 images. The model achieved an average accuracy of 94.2%, with a precision of 92.7%, recall of 93.1%, and F1-score of 92.9% across 10-fold cross-validation. Each verified genuine product is minted as a unique ERC-721 token on the Ethereum blockchain, with associated metadata retrievable via QR-code-based verification. The system demonstrated a True Verification Rate (TVR) of 95.3%, a False Acceptance Rate (FAR) of 4.1%, and an average verification latency of 1.42 seconds, while maintaining minimal gas costs on the Goerli test net. The study introduces a scalable, tamper-proof, and transparent product authentication framework suitable for industries such as pharmaceuticals, fashion, and electronics. By bridging intelligent classification with decentralized verification, this research advances the field of anti-counterfeit technology and lays a foundation for real-world blockchain-integrated supply chain systems.
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