Position: 3D Gaussian Splatting Watermarking Should Be Scenario-Driven and Threat-Model Explicit
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866915770080952320 |
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| author | Deng, Yangfan Nakra, Anirudh Wu, Min |
| author_facet | Deng, Yangfan Nakra, Anirudh Wu, Min |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02602 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Position: 3D Gaussian Splatting Watermarking Should Be Scenario-Driven and Threat-Model Explicit Deng, Yangfan Nakra, Anirudh Wu, Min Cryptography and Security Machine Learning 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. |
| title | Position: 3D Gaussian Splatting Watermarking Should Be Scenario-Driven and Threat-Model Explicit |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2602.02602 |