Robust Watermarking for 3D Mesh Models Based on Geometrically Weighted Aggregation

To address the limitations of current 3D mesh watermarking in robustness and imperceptibility, this paper proposes a deep watermarking based on a geometric-weighted aggregation mechanism. The message encoder and decoder networks are first improved to enable the effective embedding of 16-bit binary watermark information. An attack simulation module is then introduced to enhance the decoder's robustness against various distortions. Additionally, an adversarial discriminator is incorporated to guide the encoder in optimizing the embedding strategy, thereby minimizing geometric distortion. Furthermore, a cross-resolution strategy is developed to enable training on low-resolution meshes and perform watermark embedding and extraction on high-resolution meshes. Experimental results demonstrate that it outperforms the existing mainstream approaches in terms of extraction accuracy, geometric fidelity, and imperceptibility.