All That Glitters Is Not Gold: Key-Secured 3D Secrets within 3D Gaussian Splatting

2025-03-10

AI Summary

Existing 3D Gaussian Splatting (3DGS) steganography methods struggle to balance imperceptibility and reconstruction fidelity, while suffering from suboptimal feature utilization and vulnerability to statistical detection. Method: We propose the first end-to-end, key-controllable 3DGS steganography framework. Contributions/Results: (1) A key-driven multi-secret embedding mechanism enables fine-grained access control and secure secret distribution; (2) We characterize the heterogeneous contributions of Gaussian ellipsoid parameters to steganographic distortion, guiding optimal feature-space selection; (3) We introduce the 3D-Sinkhorn distance to quantify geometric distribution perturbations in 3D space, establishing the first differentiable metric for 3D steganographic imperceptibility. Experiments demonstrate that our method achieves state-of-the-art reconstruction quality (PSNR/SSIM) while significantly enhancing robustness against statistical steganalysis—simultaneously ensuring high-fidelity cover reconstruction and high-accuracy secret recovery.

Technology Category

Computer Vision: 3D Computer VisionSearch and Optimization: Distributed SearchMachine Learning: Learning with Manifolds

Application Category

Security and Privacy: Large-scale security measurementsGraph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent advances in 3D Gaussian Splatting (3DGS) have revolutionized scene reconstruction, opening new possibilities for 3D steganography by hiding 3D secrets within 3D covers. The key challenge in steganography is ensuring imperceptibility while maintaining high-fidelity reconstruction. However, existing methods often suffer from detectability risks and utilize only suboptimal 3DGS features, limiting their full potential. We propose a novel end-to-end key-secured 3D steganography framework (KeySS) that jointly optimizes a 3DGS model and a key-secured decoder for secret reconstruction. Our approach reveals that Gaussian features contribute unequally to secret hiding. The framework incorporates a key-controllable mechanism enabling multi-secret hiding and unauthorized access prevention, while systematically exploring optimal feature update to balance fidelity and security. To rigorously evaluate steganographic imperceptibility beyond conventional 2D metrics, we introduce 3D-Sinkhorn distance analysis, which quantifies distributional differences between original and steganographic Gaussian parameters in the representation space. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both cover and secret reconstruction while maintaining high security levels, advancing the field of 3D steganography. Code is available at https://github.com/RY-Paper/KeySS

Problem

• Ensures imperceptibility and high-fidelity in 3D steganography.
• Addresses detectability risks and suboptimal feature use in 3DGS.
• Introduces key-secured framework for multi-secret hiding and access control.

Innovation

• Key-secured 3D steganography framework
• Key-controllable multi-secret hiding mechanism
• 3D-Sinkhorn distance for imperceptibility analysis