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Spectral Defense Against Resource-Targeting Attack in 3D Gaussian Splatting

Yang Chen, Yi Yu, Jiaming He, Yueqi Duan, Zheng Zhu, Yap-Peng Tan

Abstract

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, ultimately causing unstable Gaussian overgrowth and degraded scene fidelity. To address this, we propose \textbf{Spectral Defense} in Gaussian and image fields. We first design a 3D frequency filter to selectively prune Gaussians exhibiting abnormally high frequencies. Since natural scenes also contain legitimate high-frequency structures, directly suppressing high frequencies is insufficient, and we further develop a 2D spectral regularization on renderings, distinguishing naturally isotropic frequencies while penalizing anisotropic angular energy to constrain noisy patterns. Experiments show that our defense builds robust, accurate, and secure 3DGS, suppressing overgrowth by up to $5.92\times$, reducing memory by up to $3.66\times$, and improving speed by up to $4.34\times$ under attacks.

Spectral Defense Against Resource-Targeting Attack in 3D Gaussian Splatting

Abstract

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, ultimately causing unstable Gaussian overgrowth and degraded scene fidelity. To address this, we propose \textbf{Spectral Defense} in Gaussian and image fields. We first design a 3D frequency filter to selectively prune Gaussians exhibiting abnormally high frequencies. Since natural scenes also contain legitimate high-frequency structures, directly suppressing high frequencies is insufficient, and we further develop a 2D spectral regularization on renderings, distinguishing naturally isotropic frequencies while penalizing anisotropic angular energy to constrain noisy patterns. Experiments show that our defense builds robust, accurate, and secure 3DGS, suppressing overgrowth by up to , reducing memory by up to , and improving speed by up to under attacks.
Paper Structure (11 sections, 12 equations, 3 figures, 11 tables, 1 algorithm)

This paper contains 11 sections, 12 equations, 3 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminary
  4. Spectral Defense for 3DGS
  5. 3D Frequency Filter
  6. 2D Spectral Regularization
  7. Overall Pipeline
  8. Experiments
  9. Main Results
  10. Ablations
  11. Conclusion

Figures (3)

  • Figure 1: Spectral defense against attack in 3DGS. (a) A resource-targeting attack poisons input images to trigger excessive Gaussian growth, leading to redundant splats and degrading renderings. Our defense operates jointly in 3D and 2D domains, where 3D frequency filter prunes Gaussians with abnormally high-frequency responses to suppress attack-induced overgrowth, and 2D spectral regularization constrains the angular distribution of energy to reduce anisotropic noise. (b) Frequency spectrum reveals spectral energy and angular distribution to distinguish frequency components of images.
  • Figure 2: Spectral energy over frequencies under clean, poisoned, and defended settings.
  • Figure 3: Qualitative comparison under the poison setting across representative scenes.