A Comprehensive Blockchain-Based AI Model Integrity Verification System with Cryptographic Authentication, Smart Contracts, and Real-Time Anomaly Detection

Abstract

The system architecture presented in this work uses blockchain technology in conjunction with cryptographic authentication methods and real-time anomaly detection to validate the integrity of artificial intelligence models. By integrating tokenization-based model tracking, zero-knowledge proof verification, and machine learningbased integrity monitoring, the suggested solution fills important holes in current methods. We use Ethereum smart contracts to construct a prototype system and assess it using several AI model designs. In comparison to signature-based methods alone, experimental findings show 96.7% fewer false positives, sub-second verification latency for the real-time component, and 99.4% detection accuracy for model tampering attempts. Every day, the system effectively processes 10,000 model inference records while upholding cryptographic security requirements.