A Blockchain-Based Copyright Governance Framework for AIGC Content with Perceptual Hashing Tracing

Abstract

The rapid development of AIGC poses unprecedented challenges to digital copyright management, including training data disputes, originality determination, and derivative tracing. This paper proposes a novel copyright governance framework integrating perceptual hashing and blockchain for AIGC content. A fine-tuned CNN generates robust perceptual hashes that capture AIGC characteristics while maintaining consistency across variations. The framework uses contrastive and adversarial training. Hashes are registered on a blockchain with smart contracts for automated management. A hierarchical tracing mechanism links generated content to training data sources via multi-level similarity analysis. Experiments on a comprehensive AIGC dataset show 98.7% identification accuracy, 97.8% average hash consistency, and 94.5% tracing accuracy for derivative works, addressing critical gaps in digital rights management.