StealthAttack

Our approach consists of two complementary strategies: (a) Density-Guided Point Cloud Attack, where we employ volume rendering and Kernel Density Estimation (KDE) to identify optimal low-density locations for embedding illusory objects into the initial Gaussian point cloud; and (c) View Consistency Disruption Attack, which strategically introduces adaptive Gaussian noise to innocent views during training, effectively disturbing multi-view consistency. (b) illustrates the standard 3D Gaussian Splatting (3DGS) training pipeline for reference. The combined strategies successfully inject convincing illusions from poisoned views while maintaining high fidelity in innocent viewpoints.

There are exciting concurrent works exploring different attack vectors in 3DGS:

• Poison-splat: Computation Cost Attack on 3D Gaussian Splatting.
• GaussTrap: Stealthy Poisoning Attacks on 3D Gaussian Splatting for Targeted Scene Confusion.

While Poison-Splat targets computational costs, our work embeds visible illusions. GaussTrap modifies training for scene confusion. Our density-guided approach focuses on data poisoning.

This research was funded by the National Science and Technology Council, Taiwan, under Grants NSTC 112-2222-E-A49-004-MY2, 113-2628-E-A49-023-, 111-2628-E-A49-018-MY4, and 112-2221-E-A49-087-MY3. The authors are grateful to Google, NVIDIA, and MediaTek Inc. for their generous donations. Yu-Lun Liu acknowledges the Yushan Young Fellow Program by the MOE in Taiwan.