Spectral Defense Against Resource-Targeting Attack in 3D Gaussian Splatting
Recent advances in 3D Gaussian Splatting (3DGS) deliver high-quality rendering, yet the Gaussian representation exposes a new attack surface, the resource-targeting attack. This attack poisons training images, excessively inducing Gaussian growth to cause resource exhaustion. Although efficiency-oriented methods such as smoothing, thresholding, and pruning have been explored, these spatial-domain strategies operate on visible structures but overlook how stealthy perturbations distort the underlying spectral behaviors of training data. As a result, poisoned inputs introduce abnormal high-frequency amplifications that mislead 3DGS into interpreting noisy patterns as detailed structures, ult...
| Authors | Yang Chen, Yi Yu, Jiaming He et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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