3D Gaussian Splatting (3DGS) has already become the emerging research focus in the fields of 3D scene reconstruction and novel view synthesis. Given that training a 3DGS requires a significant amount of time and computational cost, it is crucial to protect the copyright, integrity, and privacy of such 3D assets. Steganography, as a crucial technique for encrypted transmission and copyright protection, has been extensively studied. However, it still lacks profound exploration targeted at 3DGS. Unlike its predecessor NeRF, 3DGS possesses two distinct features: 1) explicit 3D representation; and 2) real-time rendering speeds. These characteristics result in the 3DGS point cloud files being public and transparent, with each Gaussian point having a clear physical significance. Therefore, ensuring the security and fidelity of the original 3D scene while embedding information into the 3DGS point cloud files is an extremely challenging task. To solve the above-mentioned issue, we first propose a steganography framework for 3DGS, dubbed GS-Hider, which can embed 3D scenes and images into original GS point clouds in an invisible manner and accurately extract the hidden messages. Specifically, we design a coupled secured feature attribute to replace the original 3DGS's spherical harmonics coefficients and then use a scene decoder and a message decoder to disentangle the original RGB scene and the hidden message. Extensive experiments demonstrated that the proposed GS-Hider can effectively conceal multimodal messages without compromising rendering quality and possesses exceptional security, robustness, capacity, and flexibility.
三维高斯喷溅技术（3DGS）已成为三维场景重建和新视角合成领域的新兴研究焦点。鉴于训练3DGS需要大量时间和计算成本，保护这类三维资产的版权、完整性和隐私至关重要。隐写术作为加密传输和版权保护的关键技术已经被广泛研究。然而，针对3DGS的深入探索仍然不足。与其前身NeRF不同，3DGS具有两个独特特征：1) 明确的三维表示；和2) 实时渲染速度。这些特性导致3DGS点云文件公开且透明，每个高斯点都具有明确的物理意义。因此，在将信息嵌入3DGS点云文件的同时确保原始三维场景的安全性和保真度是一项极其具有挑战性的任务。为了解决上述问题，我们首次提出一个针对3DGS的隐写框架，称为GS-Hider，它可以以隐形方式将三维场景和图像嵌入原始GS点云中，并准确提取隐藏信息。具体来说，我们设计了一个耦合的安全特征属性来替换原始3DGS的球谐系数，然后使用场景解码器和信息解码器来分离原始RGB场景和隐藏信息。广泛的实验表明，所提出的GS-Hider能够有效地隐藏多模态信息，同时不损害渲染质量，并且具有卓越的安全性、鲁棒性、容量和灵活性。