ChainMark: Integrating an Invertible Neural Network and Blockchain for Ensuring Ownership Rights in Image Watermarking

Abstract

The widespread use of generative AI has intensified issues related to digital ownership, even as it enhances the efficiency of digital content creation. While watermarking is a primary method for protecting these rights, existing neural network-based approaches often prioritize robustness and imperceptibility, neglecting verifiable ownership. To address this limitation, this paper proposes ChainMark, a system that integrates an invertible neural network (INN) with blockchain technology. ChainMark employs an INN trained within the discrete wavelet transform domain to embed watermarks that are resilient to diverse signal processing attacks. Crucially, unlike traditional approaches that rely solely on watermark extraction, the proposed system secures the verification process through a blockchain smart contract. Experimental results validate the system's theoretical security and demonstrate that the joint LH-HL model configuration achieves an optimal trade-off between visual quality and extraction accuracy. Consequently, ChainMark effectively guarantees creator rights by ensuring both high-performance watermarking and trustworthy ownership verification.