Robust Image Watermarking via Clustered Visual State-Space Modeling

Most existing DNN-based image watermarking methods adopt an “encoder–noise–decoder” paradigm, where the watermark is typically replicated and expanded in a straightforward manner and then directly fused with image features, which limits robustness under complex distortions. Although Transformers improve fusion via attention mechanisms, their quadratic computational complexity makes high-resolution processing prohibitively expensive. To address these issues, we propose CCViM, a robust watermarking framework built on Vision Mamba, which leverages the linear-complexity property of state-space models (SSMs) to enable efficient global interactions. We design a Watermark Representation Learning Module (WRLM) that performs hierarchical feature extraction and structured expansion of the watermark through cascaded VSS blocks, yielding semantically rich and perturbation-resistant watermark representations. In addition, we introduce an Interwoven Fusion Enhancement Module (IFEM), which employs a CCS6 structure to treat the watermark as a dynamic guidance signal. By combining contextual clustering with the Mamba mechanism, IFEM deeply interweaves the watermark into host features at both local and global levels. Experiments on COCO, DIV2K, and ImageNet demonstrate that CCViM consistently improves imperceptibility, robustness, and efficiency to varying degrees, and remains stable and high quality under attacks such as JPEG compression, cropping, and Gaussian blur.