3D content acquisition and creation are expanding rapidly in the new era of machine learning and AI. 3D Gaussian Splatting (3DGS) has become a promising high-fidelity and real-time representation for 3D content. Similar to the initial wave of digital audio-visual content at the turn of the millennium, the demand for intellectual property protection is also increasing, since explicit and editable 3D parameterization makes unauthorized use and dissemination easier. In this position paper, we argue that effective progress in watermarking 3D assets requires articulated security objectives and realistic threat models, incorporating the lessons learned from digital audio-visual asset protection over the past decades. To address this gap in security specification and evaluation, we advocate a scenario-driven formulation, in which adversarial capabilities are formalized through a security model. Based on this formulation, we construct a reference framework that organizes existing methods and clarifies how specific design choices map to corresponding adversarial assumptions. Within this framework, we also examine a legacy spread-spectrum embedding scheme, characterizing its advantages and limitations and highlighting the important trade-offs it entails. Overall, this work aims to foster effective intellectual property protection for 3D assets.

翻译：在机器学习与人工智能的新时代，三维内容获取与创作正迅速扩展。三维高斯泼溅（3DGS）已成为一种具有前景的高保真实时三维内容表示方法。与千禧年初数字视听内容的首次浪潮类似，由于显式且可编辑的三维参数化使得未经授权的使用与传播更为容易，对知识产权保护的需求也日益增长。在本立场文件中，我们认为，三维资产水印技术的有效进展需要明确的安全目标与现实的威胁模型，并应汲取过去数十年数字视听资产保护的经验教训。为弥补安全规范与评估方面的不足，我们倡导一种场景驱动的构建方法，其中攻击者能力通过安全模型进行形式化描述。基于此构建方式，我们建立了一个参考框架，用于梳理现有方法，并阐明特定设计选择如何映射到相应的对抗性假设。在此框架内，我们还分析了一种传统的扩频嵌入方案，阐述了其优势与局限，并强调了其所涉及的重要权衡。总体而言，本研究旨在促进三维资产的有效知识产权保护。