GuardMarkGS: Unified Ownership Tracing and Edit Deterrence for 3D Gaussian Splatting
Utae Jeong, Jaewan Choi, Junseok Lee, Jongheon Jeong, Sang Ho Yoon, ByoungSoo Koh, Sangpil Kim

TL;DR
This paper introduces GuardMarkGS, a unified framework that combines watermarking and adversarial techniques to protect 3D Gaussian Splatting assets from unauthorized use and editing.
Contribution
It is the first method to jointly optimize ownership tracing and edit deterrence for 3D Gaussian Splatting, balancing protection and rendering quality.
Findings
Achieves a good balance between watermark accuracy and edit deterrence.
Demonstrates effectiveness on scenes from Mip-NeRF 360 and Instruct-NeRF2NeRF.
Improves copyright protection for 3D assets through integrated optimization.
Abstract
3D Gaussian Splatting (3DGS) is becoming a practical representation for novel view synthesis, but its growing adoption, together with rapid advances in instruction-driven 3DGS editing, also exposes a dual copyright risk: once a 3DGS-based asset is released, it can be used without permission and manipulated through 3D editing. Existing protection methods address only one side of this problem. Watermarking can trace ownership after unauthorized use, but it cannot prevent malicious editing. Adversarial edit-deterrence methods can disrupt editing, but they do not provide evidence of ownership. To the best of our knowledge, we present the first unified protection framework for 3DGS that jointly optimizes ownership tracing and unauthorized editing deterrence. Our framework combines a scene-wide watermarking objective over all Gaussians with an adversarial objective for edit deterrence. The…
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