GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting
| Autor: | Chen, Zixuan, Wang, Guangcong, Zhu, Jiahao, Lai, Jianhuang, Xie, Xiaohua |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 3D Gaussian Splatting (3DGS) has recently created impressive assets for various applications. However, the copyright of these assets is not well protected as existing watermarking methods are not suited for 3DGS considering security, capacity, and invisibility. Besides, these methods often require hours or even days for optimization, limiting the application scenarios. In this paper, we propose GuardSplat, an innovative and efficient framework that effectively protects the copyright of 3DGS assets. Specifically, 1) We first propose a CLIP-guided Message Decoupling Optimization module for training the message decoder, leveraging CLIP's aligning capability and rich representations to achieve a high extraction accuracy with minimal optimization costs, presenting exceptional capability and efficiency. 2) Then, we propose a Spherical-harmonic-aware (SH-aware) Message Embedding module tailored for 3DGS, which employs a set of SH offsets to seamlessly embed the message into the SH features of each 3D Gaussian while maintaining the original 3D structure. It enables the 3DGS assets to be watermarked with minimal fidelity trade-offs and prevents malicious users from removing the messages from the model files, meeting the demands for invisibility and security. 3) We further propose an Anti-distortion Message Extraction module to improve robustness against various visual distortions. Extensive experiments demonstrate that GuardSplat outperforms the state-of-the-art methods and achieves fast optimization speed. Comment: Project page: https://narcissusex.github.io/GuardSplat and Code: https://github.com/NarcissusEx/GuardSplat |
| Databáze: | arXiv |
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