RoSMM: A Robust and Secure Multi-Modal Watermarking Framework for Diffusion Models

Current image watermarking technologies are predominantly categorized into text watermarking techniques and image steganography; however, few methods can simultaneously handle text and image-based watermark data, which limits their applicability in complex digital environments. This paper introduces an innovative multi-modal watermarking approach, drawing on the concept of vector discretization in encoder-based vector quantization. By constructing adjacency matrices, the proposed method enables the transformation of text watermarks into robust image-based representations, providing a novel multi-modal watermarking paradigm for image generation applications. Additionally, this study presents a newly designed image restoration module to mitigate image degradation caused by transmission losses and various noise interferences, thereby ensuring the reliability and integrity of the watermark. Experimental results validate the robustness of the method under multiple noise attacks, providing a secure, scalable, and efficient solution for digital image copyright protection.