A Decentralized Framework for User Generated Content Recognition via Holographic Watermark

This study presents a blockchain-based framework for user-generated content (UGC) validation. The methodology combines a bootstrapped holographic watermarking procedure with temporally distributed payloads, SHA-256 hashing linked to an Ethereum-compatible ledger, and a lightweight custom 3D CNN model for watermark detection. The blockchain layer functions as an audit trail, confirming ownership and integrity of the content. Meanwhile, watermark detection through the Model-as-a-Key (MaaK) paradigm provides a complementary mechanism for identifying embedded signatures when content alterations occur. To evaluate the proposed framework, we define intentional and non-intentional threat models on a subset of the Kinetics dataset, containing user-generated videos. Considering a mixed clean–noisy evaluation set, the custom watermark detector achieves an overall accuracy of \(91\%\) with an ROC–AUC of \(0.96\) , while presenting a mean accuracy of \(81\%\) under advanced threat conditions, indicating stable performance across multiple transformations. The ablation study, which compares our model with the pretrained VGG-16/19 and ResNet-50, highlights improved efficiency–robustness trade-offs within the integrated verification pipeline. In conclusion, we demonstrate the feasibility of combining blockchain with AI-based watermark recognition for supporting user-generated content authenticity verification in decentralized environments.