Mesh Watermark Removal Attack and Mitigation: A Novel Perspective of Function Space
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Abstract (arXiv)
Mesh watermark embeds secret messages in 3D meshes and decodes the message from watermarked meshes for ownership verification. Current watermarking methods directly hide secret messages in vertex and face sets of meshes. However, mesh is a discrete representation that uses vertex and face sets to describe a continuous signal, which can be discretized in other discrete representations with different vertex and face sets. This raises the question of whether the watermark can still be verified on the different discrete representations of the watermarked mesh. We conduct this research in an attack-then-defense manner by proposing a novel function space mesh watermark removal attack FuncEvade and then mitigating it through function space mesh watermarking FuncMark. In detail, FuncEvade generates a different discrete representation of a watermarked mesh by extracting it from the signed distance function of the watermarked mesh. We observe that the generated mesh can evade ALL previous watermarking methods. FuncMark mitigates FuncEvade by watermarking signed distance function through message-guided deformation. Such deformation can survive isosurfacing and thus be inherited by the extracted meshes for further watermark decoding. Extensive experiments demonstrate that FuncEvade achieves 100% evasion rate among all previous watermarking methods while achieving only 0.3% evasion rate on FuncMark. Besides, our FuncMark performs similarly on other metrics compared to state-of-the-art mesh watermarking methods.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 18 Nov 2023.
Builds on
13 of 30 references.
References · 13
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
- 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
- 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
- 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 390
- 2021Editing Conditional Radiance Fieldscited by 222
- 2022From data to functa: Your data point is a function and you can treat it like onecited by 187
- 2020Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondencecited by 119
- 2023Evading Watermark based Detection of AI-Generated Contentcited by 78
- 2019Controlling Neural Level Setscited by 76
- 2020Convolutional Generation of Textured 3D Meshescited by 52
- 2021Deep 3D Mesh Watermarking with Self-Adaptive Robustnesscited by 6
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 2.
3 papers.
- 2026Geometry-Preserving Blind Watermarking for Raw 3D Point Clouds26 Sep 2026
- 2026NGS-Marker: Robust Native Watermarking for 3D Gaussian Splatting18 Aug 2026
- 2024GS-Hider: Hiding Messages into 3D Gaussian Splatting24 May 2024
Similar, not cited
Close in wording, no citation either way
- 2021Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings
- 2026Geometry-Aware Localized Watermarking for Copyright Protection in Embedding-as-a-Service
- 2025X-SG2S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
- 2024GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
- 2024GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting
- 2025Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks
- 2024Towards Secure and Usable 3D Assets: A Novel Framework for Automatic Visible Watermarking
- 2024WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking