ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting

2025-01-07

AI Summary

3D Gaussian Splatting (3D-GS) models lack effective implicit copyright protection mechanisms. Method: This work proposes the first lossless, reversible steganographic framework tailored for 3D-GS, innovatively integrating knowledge distillation, differentiable rendering, and gradient-guided optimization to robustly embed and precisely extract copyright information within the 3D-GS parameter space—thereby overcoming inherent constraints imposed by the NeRF paradigm on steganographic design. Contribution/Results: The method achieves near-100% bit recovery accuracy across diverse scenes, with rendering PSNR degradation below 0.1 dB and no perceptible visual distortion. It establishes the first implicit watermarking paradigm for neural radiance field–based 3D representations that simultaneously ensures high-fidelity reconstruction and strong robustness, filling a critical technical gap in copyright protection for 3D-GS models.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• 3D Gaussian Splattering
• Invisible Information Embedding
• Copyright Protection

Innovation

• 3D Gaussian Splattering
• Invisible Copyright Information
• Visual Quality Preservation