mlsanityData to 14 Oct 2026

Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images

arXiv 2410.22705 ↗First posted 30 Oct 2024cs.CVcited by 15

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Abstract (arXiv)

Single-view 3D reconstruction methods like Triplane Gaussian Splatting (TGS) have enabled high-quality 3D model generation from just a single image input within seconds. However, this capability raises concerns about potential misuse, where malicious users could exploit TGS to create unauthorized 3D models from copyrighted images. To prevent such infringement, we propose a novel image protection approach that embeds invisible geometry perturbations, termed "geometry cloaks", into images before supplying them to TGS. These carefully crafted perturbations encode a customized message that is revealed when TGS attempts 3D reconstructions of the cloaked image. Unlike conventional adversarial attacks that simply degrade output quality, our method forces TGS to fail the 3D reconstruction in a specific way - by generating an identifiable customized pattern that acts as a watermark. This watermark allows copyright holders to assert ownership over any attempted 3D reconstructions made from their protected images. Extensive experiments have verified the effectiveness of our geometry cloak. Our project is available at https://qsong2001.github.io/geometry_cloak.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 43 of its 53 references that are in the map. First posted on arXiv 30 Oct 2024.

Builds on

43 of 53 references.

References · 43

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  6. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  7. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  8. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  9. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,489
  10. 2022TensoRF: Tensorial Radiance Fieldscited by 1,280
  11. 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 849
  12. 2024LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creationcited by 739
31 more
  1. 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 612
  2. 2018HiDDeN: Hiding Data With Deep Networkscited by 397
  3. 2023GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splattingcited by 335
  4. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 326
  5. 2023Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformerscited by 258
  6. 2020The Creation and Detection of Deepfakes: A Surveycited by 247
  7. 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Modelscited by 205
  8. 2023GaussianEditor: Editing 3D Gaussians Delicately with Text Instructionscited by 168
  9. 2023Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examplescited by 167
  10. 2023Raising the Cost of Malicious AI-Powered Image Editingcited by 163
  11. 2022NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360deg Viewscited by 148
  12. 2023Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generationcited by 133
  13. 2023Anti-DreamBooth: Protecting users from personalized text-to-image synthesiscited by 120
  14. 2023EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protectioncited by 111
  15. 2021LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognitioncited by 94
  16. 2022Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfercited by 78
  17. 2023CopyRNeRF: Protecting the CopyRight of Neural Radiance Fieldscited by 34
  18. 2022StegaNeRF: Embedding Invisible Information within Neural Radiance Fieldscited by 32
  19. 2024Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Modelcited by 18
  20. 2023InstructP2P: Learning to Edit 3D Point Clouds with Text Instructionscited by 14
  21. 2023NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbationscited by 10
  22. 2023Targeted Adversarial Attacks on Generalizable Neural Radiance Fieldscited by 10
  23. 2024Purified and Unified Steganographic Networkcited by 10
  24. 2024Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problemscited by 8
  25. 2024Spy-Watermark: Robust Invisible Watermarking for Backdoor Attackcited by 7
  26. 2024GeometrySticker: Enabling Ownership Claim of Recolorized Neural Radiance Fieldscited by 6
  27. 2023Steganography of Steganographic Networkscited by 5
  28. 2023Shielding the Unseen: Privacy Protection through Poisoning NeRF with Spatial Deformationcited by 4
  29. 2024IPR-NeRF: Ownership Verification meets Neural Radiance Fieldcited by 3
  30. 2024Event Trojan: Asynchronous Event-based Backdoor Attackscited by 3
  31. 2023Securing Fixed Neural Network Steganographycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 5.

15 papers.

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  1. 2025ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting
  2. 2024GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
  3. 2024WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking
  4. 2025Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?
  5. 2025X-SG2S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
  6. 2026Position: 3D Gaussian Splatting Watermarking Should Be Scenario-Driven and Threat-Model Explicit
  7. 2026GuardMarkGS: Unified Ownership Tracing and Edit Deterrence for 3D Gaussian Splatting
  8. 20243D-GSW: 3D Gaussian Splatting for Robust Watermarking