  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. Our project is available at:
https://xuanyuzhang21.github.io/project/gshider.
