Exploration of Thangka Identification and Traceability Mechanism Empowered by Blockchain

Authenticity verification for thangka artworks remains challenging in the market, as traditional physical authentication methods offer limited reliability, while modern spectroscopic and chemical testing technologies are costly and unsuitable for large-scale application. Although deep learning methods can achieve efficient authentication through image features, they rely on centralized databases to store feature information, making them susceptible to tampering risks and undermining the credibility of authentication results. To address these issues, this study proposes a digital authentication method for thangka paintings that integrates blockchain technology. This approach stores image features in the InterPlanetary File System (IPFS) and records their hash values on the blockchain, ensuring the immutability and verifiable evidence of feature data. Simultaneously, it employs convolutional neural networks for feature extraction and similarity analysis of thangka images, constructing an integrated platform system encompassing storage, authentication, and traceability. This enhances the reliability and automation of authentication outcomes. The platform further supports full-process traceability of thangka storage and authentication operations, providing a viable pathway for establishing a scientific and reliable digital authentication system for thangkas. Experimental evaluation on a dataset of 2847 thangka images demonstrates 99.2% authentication accuracy, with a precision of 98.7% and an F1-score of 99.1%, while end-to-end authentication latency averages 1247 ms, validating the system’s effectiveness for practical museum and market deployment scenarios.