GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting

Abstract: 3D Gaussian Splatting (3DGS) has become a crucial method for acquiring 3D assets. To protect the copyright of these assets, digital watermarking techniques can be applied to embed ownership information discreetly within 3DGS models. However, existing watermarking methods for meshes, point clouds, and implicit radiance fields cannot be directly applied to 3DGS models, as 3DGS models use explicit 3D Gaussians with distinct structures and do not rely on neural networks. Naively embedding the watermark on a pre-trained 3DGS can cause obvious distortion in rendered images. In our work, we propose an uncertainty-based method that constrains the perturbation of model parameters to achieve invisible watermarking for 3DGS. At the message decoding stage, the copyright messages can be reliably extracted from both 3D Gaussians and 2D rendered images even under various forms of 3D and 2D distortions. We conduct extensive experiments on the Blender, LLFF and MipNeRF-360 datasets to validate the effectiveness of our proposed method, demonstrating state-of-the-art performance on both message decoding accuracy and view synthesis quality.

• The paper introduces an uncertainty-aware watermarking technique that embeds watermarks into 3D Gaussian structures based on parameter uncertainty.
• It employs dual decoders—a 3D PointNet and a 2D HiDDeN network—to robustly extract hidden messages even under distortions like noise, blurring, and cropping.
• Experimental results across multiple datasets show state-of-the-art performance in watermark robustness and image quality metrics such as PSNR, SSIM, and LPIPS.

Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting

The paper under review addresses a significant gap in the field of digital asset protection, specifically concerning 3D Gaussian Splatting (3DGS) models. Unlike traditional 3D model representations such as meshes or Neural Radiance Fields (NeRF), 3DGS employs explicit 3D Gaussian structures, presenting unique challenges and opportunities in copyright protection. This work provides a comprehensive method—GaussianMarker—that ensures the protection of copyright by embedding watermarks into the explicit Gaussians used in 3DGS models. The research introduces an uncertainty-aware approach to this end, demonstrating both the invisibility of watermarks and their robustness against distortions.

Key Contributions

The paper's primary contribution is the proposed method that utilizes uncertainty estimation to determine appropriate locations for embedding watermarks within 3D Gaussian parameters. By assessing the uncertainty of each parameter, the approach cleverly densifies Gaussians with high uncertainty, allowing for more significant perturbations without perceivable impacts on rendered images. This strategy provides a solution to maintain both the aesthetic integrity of the 3D models and their derivative 2D renderings.

Moreover, the work extends the watermarking framework by incorporating both 3D and 2D message decoders. The 3D message decoder utilizes a PointNet architecture, effectively handling the explicit point cloud-like structure of 3DGS models. In contrast, the 2D message decoder is based on a pre-trained HiDDeN network, which serves as the basis for transferring knowledge from image to 3D space. This dual decoder setup ensures robust watermarks that can be reliably extracted, even under various forms of adversarial distortions such as Gaussian noise, blurring, or cropping in both spatial domains.

Experimental Validation

The paper presents compelling experimental results across multiple datasets, including Blender, LLFF, and MipNeRF-360, illustrating the superior performance of their method over existing baselines. Key performance metrics like PSNR, SSIM, and LPIPS are used to gauge reconstruction quality, while bit accuracy assesses watermark robustness. The proposed method outperforms others by achieving state-of-the-art results in message decoding accuracy and maintaining high-quality view synthesis.

Notably, the experiments demonstrate that GaussianMarker does not significantly degrade the visual quality of the images while ensuring message decodability—a balance previous methods struggled to maintain, especially for explicit 3D representations like 3DGS.

Implications and Future Work

This work advances the field of digital asset protection by introducing an effective mechanism for safeguarding 3DGS models. The theoretical underpinning of using uncertainty as a criterion for perturbation tolerance is an insightful addition, likely to inspire further research into uncertainty-aware techniques within digital watermarking and beyond.

Practically, this methodology could enhance the security of proprietary 3D content in industries ranging from entertainment to engineering. The approach is novel enough to suggest potential avenues for more generalized frameworks in uncertainty-aware copyright protection across various types of data representations.

Additionally, future research could explore the application of this approach to dynamic 3DGS scenarios or other unexplored forms of model representation, thereby broadening the operational scope and refining the technique's robustness against emerging threats in digital content manipulation. Integration with model compression techniques is another promising direction, potentially alleviating any computational overhead introduced by the watermarking process.

Conclusion

In summary, the paper introduces GaussianMarker, an innovative approach to embedding watermarks in 3D Gaussian Splatting models. By leveraging uncertainty-aware perturbations, the method successfully balances the often conflicting requirements of invisibility and robustness, marking a significant step forward in digital copyright protection. The potential implications of this work and its future developments promise to enhance the security of 3D digital assets across a variety of applications.