MVPISplat: Multi-View Photometric Inconsistency for Defending 3D Gaussian Splatting Attacks

3D Gaussian Splatting (3DGS) achieves real-time photorealistic novel-view synthesis but is vulnerable to adversarial perturbations of its training images. Recent attacks inflate the Gaussian count to exhaust GPU memory or corrupt rendered scenes to mislead downstream classifiers. We find that both attacks have the same structural signature: 2D image adversarial perturbations are not 3D-consistent by design. The Gaussians generated to match them provide erroneous contributions in some views while making significant contributions in others. We propose Multi-View Photometric Inconsistency (MVPI) Defense that scores each Gaussian by the variance of its opacity-weighted local $\mathcal{L}_1$ contribution across a stratified set of training views. The most inconsistent are soft-pruned via 3DGS's existing opacity threshold method. We evalute our approach on two different datasets. MVPI reduces peak Gaussian count by up to $1.90\times$ and training time by $1.22{-}1.50\times$ under Poison-Splat attack. It also restores top-1 classification accuracy on adversarially perturbed renders from $57.4\%$ to $66.5\%$. Our code is available at https://anonymous.4open.science/r/PruneDefense-2876/README.md.