All That Glitters Is Not Gold: Key-Secured 3D Secrets within 3D Gaussian Splatting
This work addresses the challenge of secure and imperceptible 3D steganography for applications in digital content protection, though it appears incremental as it builds on existing 3DGS methods.
The paper tackles the problem of hiding 3D secrets within 3D Gaussian Splatting scenes by proposing a key-secured framework that jointly optimizes reconstruction and security, achieving state-of-the-art performance in both cover and secret fidelity while introducing a 3D-Sinkhorn distance metric for imperceptibility evaluation.
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