X-SG²S：具有X维水印的安全且可泛化的高斯溅射技术
X-SG$^2$S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
AI总结：
本文提出X-SG²S框架，为3D高斯溅射技术实现1至3维水印的多模态嵌入，兼具高保真度与鲁棒性，且无需修改参数或管线。
摘要：
3D高斯溅射（3DGS）已广泛应用于三维重建与三维生成领域。然而，3D高斯溅射的快速普及引发了人们对信息泄露与未授权使用的日益担忧，推动了有效水印技术的探索。现有方法存在容量低、在几何扰动下脆弱、需进行代价高昂的微调或管线修改等问题，因此亟需一种可泛化的前馈框架，该框架能以最小侵入性实现鲁棒的多模态嵌入。本文提出一种新框架X-SG²S，可同时为版权保护注入1维至3维水印，同时保持原始3DGS场景的高保真度。具体而言，我们首先将水印拆分为消息块，开发自适应门控以选择水印消息的注入位置；随后使用XD（多维）注入头将多模态消息注入排序后的3DGS点中。为恢复水印消息，我们开发可学习门控以识别水印位置，再通过XD提取头从这些位置恢复隐藏消息。X-SG²S是首个统一1维至3维水印技术并支持在3DGS中同时嵌入多模态水印的框架，实现这一目标时仅产生最小渲染干扰，且无需对参数或管线进行任何修改。大量实验表明，X-SG²S可有效保持水印与原始3DGS的一致性，展现出对抗模型退化的鲁棒性，并维持准确的判断能力。
英文摘要：
3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. However, the rapid adoption of 3D Gaussian Splatting raises growing concerns about information leakage and unauthorized use, urging the exploration of effective watermarking techniques. However, existing methods are limited by low capacity, fragility under geometric perturbations, and the infeasible requirement for costly fine-tuning or pipeline modifications, motivating the need for a generalizable, feed-forward framework capable of robust multi-modal embedding with minimal intrusion. In this paper, we propose a new framework X-SG$^2$S which can simultaneously inject 1D to 3D watermarks for copyright protection, while keeping the high fidelity of original 3DGS scenes. Specifically, we first split the watermarks into message patches. A self-adaptive gate is developed to select the injection positions of the watermark messages. Then, we use an XD (multi-dimensional) injection head to inject multi-modal messages into sorted 3DGS points. To restore watermarking messages, a learnable gate is developed to recognize the watermarked locations, from which our XD-extraction heads are used to restore hidden messages. X-SG$^2$S is the first framework to unify 1D-to-3D watermarking and enable simultaneous multi-modal watermark embedding in 3DGS, achieving this with minimal rendering interference and zero modifications to parameters or pipelines. Extensive experiments demonstrate that X-SG$^2$S effectively preserves consistency between the watermark and the original 3DGS, exhibits robustness against model degradation, and maintains accurate judgment capabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
