ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting

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.

Key Contributions

First steganography method specifically designed for 3D Gaussian Splatting (3D-GS) representations, filling a gap left by NeRF-based and mesh-based techniques
Consistency strategy for decoder and gradient optimization approach that improves robustness of embedded information while preserving rendering quality
Evaluation across multiple application scenarios demonstrating negligible impact on 3D reconstruction quality

🛡️ Threat Analysis

Output Integrity Attack

ConcealGS watermarks 3D-GS content assets (analogous to image/video watermarking) to enable copyright verification and provenance tracking of 3D scenes. The watermark is embedded in the content representation (Gaussian splat parameters) to authenticate ownership of the 3D asset — this is content provenance watermarking, not ML model IP protection.