Channel Interaction Mamba-Guided Generative Adversarial Network for Depth-Image-Based Rendering 3D Image Watermarking

In the field of 3D technology, depth-image-based rendering (DIBR) has been widely adopted due to its inherent advantages including low data volume and strong compatibility. However, during network transmission of DIBR 3D images, both center and virtual views are susceptible to unauthorized copying and distribution. To protect the copyright of these images, this paper proposes a channel interaction mamba-guided generative adversarial network (CIMGAN) for DIBR 3D image watermarking. To capture cross-modal feature dependencies, a channel interaction mamba (CIM) is designed. This module enables lightweight cross-modal channel interaction through a channel exchange mechanism and leverages mamba for global modeling of RGB and depth information. In addition, a feature fusion module (FFM) is devised to extract complementary information from cross-modal features and eliminate redundant information, ultimately generating high-quality 3D image features. These features are used to generate an attention map, enhancing watermark invisibility and identifying robust embedding regions. Compared to the current state-of-the-art (SOTA) 3D image watermarking methods, the proposed watermark model shows superior performance in terms of robustness and invisibility while maintaining computational efficiency.