SecureGS: Boosting the Security and Fidelity of 3D Gaussian Splatting Steganography

ICLR 2025 Conference Submission326 Authors

Keywords: 3DGS steganography, copyright protection, watermarking

Abstract: 3D Gaussian splatting (3DGS) has emerged as a premier method for 3D representation due to its real-time rendering and high-quality outputs, underscoring the critical need to protect the copyright and privacy of 3D assets. Traditional NeRF steganography methods fail to address the explicit nature of 3DGS since its point cloud files are publicly accessible. Existing GS steganography solutions mitigate some issues but still struggle with reduced rendering fidelity, increased computational demands, and security flaws, especially in the security of the geometric structure of the visualized point cloud. To address these challenges, we propose a SecureGS, a secure and efficient 3DGS steganography framework inspired by Scaffold-GS's anchor point design and neural decoding. SecureGS utilizes a hybrid decoupled Gaussian encryption mechanism to embed offsets, scales, rotations, and RGB attributes of the hidden 3D Gaussian points within anchor point features, retrievable only by authorized users through privacy-preserving neural networks. To further enhance security, we propose a density region-aware anchor growing and pruning strategy that adaptively locates optimal hiding regions without exposing hidden information. Extensive experiments demonstrate that SecureGS significantly surpasses existing GS steganography methods in rendering fidelity, speed, and security, effectively concealing and accurately extracting 3D objects, images, and bits within original 3D scenes.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 326