An integrated framework for art image novelty detection and ownership tracing using deep Siamese networks and blockchain

Abstract

Digital artworks are increasingly distributed and shared across online platforms, raising critical challenges in both ownership verification and similarity detection. On one hand, it is difficult to establish secure, tamper-resistant records of artwork ownership in decentralized environments. On the other hand, identifying whether a newly submitted artwork is visually similar to an existing one remains a non-trivial task, especially under various artistic transformations such as style transfer, inpainting, and compositional editing. Vision Models can effectively compare image content, but they lack mechanisms to securely protect ownership. In contrast, blockchain technologies offer immutability, traceability, and decentralized data storage, yet lack the capability to evaluate visual similarity. These limitations highlight the need for an integrated solution that jointly addresses both visual similarity detection and secure ownership verification. To resolve those issues, this thesis proposes a blockchain-based artwork verification system that integrates deep visual similarity detection with decentralized ownership registration. In the proposed framework, blockchain is used to register artwork ownership and store compact image feature payloads as immutable on-chain records, while offchain deep learning models extract visual embeddings and perform similarity matching. Multiple visual models are trained and evaluated under different distance metrics, loss functions, and Siamese architectures. To improve the practicality of on-chain storage, the extracted embeddings are projected into lower dimensions, quantized into compact payloads, and then analyzed for storage feasibility and matching performance. The blockchain component is further evaluated through experiments on payload storage, update cost, retrieval efficiency, and large-scale matching simulation. Experimental results show that the ResNet50 model trained with NT-Xent loss and Euclidean distance achieves the best overall performance among the tested settings, while DeiT-small performs competitively at higher embedding dimensions. The results further indicate that quantized and compressed embeddings can significantly reduce blockchain storage cost while preserving most retrieval capability, although lower-dimensional embeddings increase the false-positive rate in the final similarity simulation.