IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting

2026-06-29

AI Summary

This work proposes the first general-purpose steganographic framework tailored for 3D Gaussian Splatting (3DGS), designed to embed meaningful 3D scene content in a lossless and imperceptible manner. By formulating 3D steganography as a feedforward Gaussian embedding process, the authors introduce the GAS network to learn a scene-agnostic embedding function that directly injects secret Gaussian attributes into carrier scenes without requiring per-scene fine-tuning. The method strategically structures 3D Gaussian attributes to align with 2D learning paradigms, substantially enhancing generalization to unseen scenes. Experimental results demonstrate that the proposed framework achieves high visual fidelity across multiple datasets while outperforming existing approaches in both payload capacity and security.

Abstract

Recent advances in deep learning have notably improved steganographic message hiding. However, designing a generalizable steganographic approach for 3D Gaussian Splatting (3DGS) that can embed meaningful 3D scene content remains challenging. In this paper, we propose IBRSteG, a generalizable framework for 3DGS steganography that enables undetectable concealment of secret scenes within a steganographic scene. Unlike existing approaches whose parameter generation is rigidly coupled with the specific scene, we formulate 3D steganography as a feed-forward 3D Gaussian embedding process that generalizes across different 3DGS scenes. To realize this, we introduce GAS (Gaussian Attributes Steganographer), a network that learns a scene-independent embedding function by injecting the attributes of secret 3D Gaussian points into a cover scene, thereby directly reconstructing the steganographic scenes without per-scene finetuning or optimization. By transforming 3D Gaussian into these structured attributes, these attributes are compatible with 2D learning paradigms and benefit from their structured nature, thereby enhancing generalization to unseen 3DGS scenes. Extensive experiments on established datasets demonstrate that IBRSteG can effectively conceal different scenes with high visual quality, and achieves superior capacity and security. Code is available at https://github.com/LingXiang2023/IBRSteG.

Problem

• 3D Gaussian Splatting
• steganography
• generalization
• 3D scene hiding
• secret embedding

Innovation

• 3D Gaussian Splatting
• Steganography
• Generalizable Embedding
• Feed-forward Network
• Scene-independent