DefenseSplat: Enhancing the Robustness of 3D Gaussian Splatting via Frequency-Aware Filtering

3D Gaussian Splatting (3DGS) has emerged as a powerfulparadigm for real-time and high-fidelity 3D reconstruction from posedimages. However, recent studies reveal its vulnerability to adversarialcorruptions in input views, where imperceptible yet consistent pertur-bations can drastically degrade rendering quality, increase training andrendering time, and inflate memory usage, even leading to server denial-of-service. In our work, to mitigate this issue, we begin by analyzingthe distinct behaviors of adversarial perturbations in the low- and high-frequency components of input images using wavelet transforms. Basedon this observation, we design a simple yet effective frequency-aware de-fense strategy that reconstructs training views by filtering high-frequencynoise while preserving low-frequency content. This approach effectivelysuppresses adversarial artifacts while maintaining the authenticity ofthe original scene. Notably, it does not significantly impair training onclean data, achieving a desirable trade-off between robustness and per-formance on clean inputs. Through extensive experiments under a widerange of attack intensities on multiple benchmarks, we demonstrate thatour method substantially enhances the robustness of 3DGS without ac-cess to clean ground-truth supervision. By highlighting and addressingthe overlooked vulnerabilities of 3D Gaussian Splatting, our work pavesthe way for more robust and secure 3D reconstructions.