StealthAttack: Robust 3D Gaussian Splatting Poisoning via Density-Guided
  Illusions
3D scene representation methods like Neural Radiance Fields (NeRF) and 3D
Gaussian Splatting (3DGS) have significantly advanced novel view synthesis. As
these methods become prevalent, addressing their vulnerabilities becomes
critical. We analyze 3DGS robustness against image-level poisoning attacks and
propose a novel density-guided poisoning method. Our method strategically
injects Gaussian points into low-density regions identified via Kernel Density
Estimation (KDE), embedding viewpoint-dependent illusory objects clearly
visible from poisoned views while minimally affecting innocent views.
Additionally, we introduce an adaptive noise strategy to disrupt multi-view
consistency, further enhancing attack effectiveness. We propose a KDE-based
evaluation protocol to assess attack difficulty systematically, enabling
objective benchmarking for future research. Extensive experiments demonstrate
our method's superior performance compared to state-of-the-art techniques.
Project page: https://hentci.github.io/stealthattack/
68df49acdf49fb0df1e03d51
Bo-Hsu Ke
68df49acdf49fb0df1e03d52
You-Zhe Xie
68df49acdf49fb0df1e03d53
Yu-Lun Liu
68df49acdf49fb0df1e03d54
Wei-Chen Chiu
StealthAttack: Robust 3D Gaussian Splatting Poisoning via Density-Guided
  Illusions
3D scene representation methods like Neural Radiance Fields (NeRF) and 3D
Gaussian Splatting (3DGS) have significantly advanced novel view synthesis. As
these methods become prevalent, addressing their vulnerabilities becomes
critical. We analyze 3DGS robustness against image-level poisoning attacks and
propose a novel density-guided poisoning method. Our method strategically
injects Gaussian points into low-density regions identified via Kernel Density
Estimation (KDE), embedding viewpoint-dependent illusory objects clearly
visible from poisoned views while minimally affecting innocent views.
Additionally, we introduce an adaptive noise strategy to disrupt multi-view
consistency, further enhancing attack effectiveness. We propose a KDE-based
evaluation protocol to assess attack difficulty systematically, enabling
objective benchmarking for future research. Extensive experiments demonstrate
our method's superior performance compared to state-of-the-art techniques.
Project page: https://hentci.github.io/stealthattack/
A novel density-guided poisoning method for 3D Gaussian Splatting enhances attack effectiveness by strategically injecting Gaussian points and disrupting multi-view consistency.
Neural Radiance Fields 3D Gaussian Splatting image-level poisoning attacks density-guided poisoning Gaussian points Kernel Density Estimation viewpoint-dependent illusory objects multi-view consistency KDE-based evaluation protocol