ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting

Yifeng Yang, Hengyu Liu, Chenxin Li, Yining Sun, Wuyang Li, Yifan Liu, Yiyang Lin, Yixuan Yuan, Nanyang Ye

Abstract: As 3D Gaussian Splatting (3D-GS) emerges as a promising technique for 3D reconstruction and novel view synthesis, offering superior rendering quality and efficiency, it becomes crucial to ensure secure transmission and copyright protection of 3D assets in anticipation of widespread distribution. While steganography has advanced significantly in common 3D media like meshes and Neural Radiance Fields (NeRF), research into steganography for 3D- GS representations remains largely unexplored. To address this gap, we propose ConcealGS, a novel 3D steganography method that embeds implicit information into the explicit 3D representation of Gaussian Splatting. By introducing a consistency strategy for the decoder and a gradient optimization approach, ConcealGS overcomes limitations of NeRF-based models, enhancing both the robustness of implicit information and the quality of 3D reconstruction. Extensive evaluations across various potential application scenarios demonstrate that ConcealGS successfully recovers implicit information with negligible impact on rendering quality, offering a groundbreaking approach for embedding invisible yet recoverable information into 3D models. This work paves the way for advanced copyright protection and secure data transmission in the evolving landscape of 3D content creation and distribution. Code is available at https://github.com/zxk1212/ConcealGS.