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

Qi Song, Ziyuan Luo, Ka Chun Cheung, Simon See, Renjie Wan*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

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.
Original languageEnglish
Title of host publication38th Conference on Neural Information Processing Systems, NeurIPS 2024
PublisherNeural Information Processing Systems Foundation
Pages1-25
Number of pages25
Publication statusPublished - Dec 2024
Event38th Conference on Neural Information Processing Systems, NeurIPS 2024 - Vancouver Convention Center , Vancouver, Canada
Duration: 9 Dec 202415 Dec 2024
https://neurips.cc/Conferences/2024
https://openreview.net/group?id=NeurIPS.cc/2024

Publication series

NameAdvances in Neural Information Processing Systems
Volume37
ISSN (Print)1049-5258
NameNeurIPS Proceedings

Conference

Conference38th Conference on Neural Information Processing Systems, NeurIPS 2024
Country/TerritoryCanada
CityVancouver
Period9/12/2415/12/24
Internet address

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