# PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction

arXiv: https://arxiv.org/abs/2604.13153 · HTML: https://mlsanity.com/paper/2604.13153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [MUSt3R: Multi-view Network for Stereo 3D Reconstruction](https://mlsanity.com/paper/2503.01661/) (2025)
- [GS-Hider: Hiding Messages into 3D Gaussian Splatting](https://mlsanity.com/paper/2405.15118/) (2024)
- [GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting](https://mlsanity.com/paper/2410.23718/) (2024)
- [Poison-splat: Computation Cost Attack on 3D Gaussian Splatting](https://mlsanity.com/paper/2410.08190/) (2024)
- [GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting](https://mlsanity.com/paper/2411.19895/) (2024)
- [Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images](https://mlsanity.com/paper/2410.22705/) (2024)
- [GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting](https://mlsanity.com/paper/2407.01301/) (2024)
- [Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects](https://mlsanity.com/paper/2412.02803/) (2024)
- [WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking](https://mlsanity.com/paper/2412.05695/) (2024)
- [GaussTrap: Stealthy Poisoning Attacks on 3D Gaussian Splatting for Targeted Scene Confusion](https://mlsanity.com/paper/2504.20829/) (2025)
- [IPA-NeRF: Illusory Poisoning Attack Against Neural Radiance Fields](https://mlsanity.com/paper/2407.11921/) (2024)
- [Kaleidoscopic Background Attack: Disrupting Pose Estimation with Multi-Fold Radial Symmetry Textures](https://mlsanity.com/paper/2507.10265/) (2025)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has recently enabled highly photorealistic 3D reconstruction from casually captured multi-view images. However, this accessibility raises a privacy concern: publicly available images or videos can be exploited to reconstruct detailed 3D models of scenes or objects without the owner's consent. We present PatchPoison, a lightweight dataset-poisoning method that prevents unauthorized 3D reconstruction. Unlike global perturbations, PatchPoison injects a small high-frequency adversarial patch, a structured checkerboard, into the periphery of each image in a multi-view dataset. The patch is designed to corrupt the feature-matching stage of Structure-from-Motion (SfM) pipelines such as COLMAP by introducing spurious correspondences that systematically misalign estimated camera poses. Consequently, downstream 3DGS optimization diverges from the correct scene geometry. On the NeRF-Synthetic benchmark, inserting a 12 X 12 pixel patch increases reconstruction error by 6.8x in LPIPS, while the poisoned images remain unobtrusive to human viewers. PatchPoison requires no pipeline modifications, offering a practical, "drop-in" preprocessing step for content creators to protect their multi-view data.