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The paper introduces PatchPoison, a novel dataset poisoning technique that uses small, high-frequency adversarial patches in multi-view images to disrupt 3D reconstruction pipelines. By injecting structured checkerboard patterns into the periphery of images, PatchPoison corrupts feature matching in Structure-from-Motion (SfM), leading to misaligned camera poses and divergence during 3D Gaussian Splatting (3DGS) optimization. Experiments on NeRF-Synthetic demonstrate a 6.8x increase in LPIPS reconstruction error with minimal visual impact on the original images.
A tiny, almost invisible checkerboard patch can completely break 3D reconstruction from multi-view image datasets, offering a simple defense against unauthorized scene modeling.
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.