用密钥保护3D高斯点云中的秘密，实现隐蔽且高保真的信息隐藏。
All That Glitters Is Not Gold: Key-Secured 3D Secrets within 3D Gaussian Splatting
- 通过密钥控制的端到端框架，联合优化3D高斯重建与解码器。
- 在保持高质量重建的同时，实现多秘密隐藏与未经授权访问防护。
- 引入3D-Sinkhorn距离评估3D隐写不可察觉性，超越传统2D指标。
最近的3D高斯点云（3DGS）进展推动了场景重建的革新，为在3D载体中隐藏3D秘密提供了新可能。隐写的核心挑战在于保证不可察觉性的同时维持高保真度重建。然而，现有方法常面临可检测风险，且仅使用次优的3DGS特征，限制了其潜力。本文提出一种新型端到端密钥保护3D隐写框架（KeySS），联合优化3DGS模型与密钥保护解码器以实现秘密重建。我们发现高斯特征在秘密隐藏中的贡献不均。该框架引入密钥可控机制，支持多秘密隐藏与未授权访问防御，并系统探索最优特征更新策略，在保真度与安全性间取得平衡。为严格评估隐写不可察觉性，我们提出3D-Sinkhorn距离分析，量化原始与隐写高斯参数在表示空间中的分布差异。大量实验表明，本方法在载体与秘密重建上均达到当前最佳性能，同时保持高水平安全，推动3D隐写领域发展。代码已开源：https://github.com/RY-Paper/KeySS
原文摘要 · Abstract (English)
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
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