MPAM-3DGS: Multi-Parametric Adversarial Manipulation for 3D Gaussian Splatting

Abstract

3D Gaussian Splatting (3DGS) is gaining popularity in fields such as robotics, autonomous driving, and virtual reality, due to its effectiveness and efficiency. Given that some tasks involve high risks, it is crucial to investigate the adversarial robustness of 3DGS and its downstream tasks—a topic that remains largely unexplored. In this study, we introduce a framework, Multi-Parametric Adversarial Manipulation for 3D Gaussian Splatting (MPAM-3DGS), that allows to attack 3DGS and its downstream tasks, such as object detection and classification, by perturbing a specified subset of parameters. Leveraging this framework, we examine the adversarial sensitivity of each 3DGS parameter and propose two strategies to attack multiple parameters based on our observations. To our knowledge, this is the first study to explore the adversarial robustness of 3DGS. Our experimental results demonstrate the effectiveness of our attacks on downstream tasks and the invisibility of perturbations in 3DGS. The code can be found at https://github.com/jiang-wenxiang/MPAM-3DGS.