Position: 3D Gaussian Splatting Watermarking Should Be Scenario-Driven and Threat-Model Explicit

2026-02-01

AI Summary

This work addresses the urgent need for effective intellectual property protection in 3D Gaussian Splatting (3DGS), which is highly susceptible to unauthorized copying. We present the first formal security framework for watermarking 3DGS by integrating scene-driven design principles with an explicit threat model, systematically organizing existing approaches and clarifying the relationships between their design choices and underlying adversarial assumptions. Leveraging this framework, we evaluate spread-spectrum embedding techniques to rigorously assess current methods, elucidating their strengths and limitations while uncovering critical performance trade-offs. Our analysis establishes a theoretical foundation and offers practical guidance for the secure design and evaluation of watermarking mechanisms tailored to 3D assets represented via 3DGS.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

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.

Problem

• 3D Gaussian Splatting
• watermarking
• intellectual property protection
• threat model
• security objectives

Innovation

• 3D Gaussian Splatting
• watermarking
• threat model
• scenario-driven
• intellectual property protection