With the rapid development of 3D reconstruction technology, the widespread distribution of 3D data has become a future trend. While traditional visual data (such as images and videos) and NeRF-based formats already have mature techniques for copyright protection, steganographic techniques for the emerging 3D Gaussian Splatting (3D-GS) format have yet to be fully explored. To address this, we propose ConcealGS, an innovative method for embedding implicit information into 3D-GS. By introducing the knowledge distillation and gradient optimization strategy based on 3D-GS, ConcealGS overcomes the limitations of NeRF-based models and enhances the robustness of implicit information and the quality of 3D reconstruction. We evaluate ConcealGS in various potential application scenarios, and experimental results have demonstrated that ConcealGS not only successfully recovers implicit information but also has almost no impact on rendering quality, providing a new approach for embedding invisible and recoverable information into 3D models in the future.

本研究针对当前3D高斯散点格式中缺乏有效版权保护手段的问题，提出了一种名为ConcealGS的新方法。该方法结合知识蒸馏和梯度优化策略，提升了隐形信息的鲁棒性及3D重建的质量。实验结果表明，ConcealGS不仅能够成功恢复隐形信息，还几乎不影响渲染质量，为未来3D模型中的隐形信息嵌入提供了新思路。

ConcealGS：在3D高斯散点图中隐藏隐形版权信息