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.
@article{arxiv.2604.13153,
title = {PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction},
author = {Prajas Wadekar and Venkata Sai Pranav Bachina and Kunal Bhosikar and Ankit Gangwal and Charu Sharma},
journal= {arXiv preprint arXiv:2604.13153},
year = {2026}
}
Comments
CVPR Workshop on Security, Privacy, and Adversarial Robustness in 3D Generative Vision Models (SPAR-3D), 2026