Abstract
With 3D Gaussian Splatting (3DGS) being increasingly used in safety-critical applications, how can an adversary manipulate the scene to cause harm? We introduce CLOAK, the first attack that leverages view-dependent Gaussian appearances - colors and textures that change with viewing angle - to embed adversarial content visible only from specific viewpoints. We further demonstrate DAGGER, a targeted adversarial attack directly perturbing 3D Gaussians without access to underlying training data, deceiving multi-stage object detectors e.g., Faster R-CNN, through established methods such as projected gradient descent. These attacks highlight underexplored vulnerabilities in 3DGS, introducing a new potential threat to robotic learning for autonomous navigation and other safety-critical 3DGS applications.
Abstract (translated)
随着3D高斯点阵（3D Gaussian Splatting，简称3DGS）在安全关键应用中的使用日益增多，对手如何才能通过操纵场景来造成危害？我们介绍了CLOAK，这是首个利用视图依赖的高斯外观——即随观察角度变化的颜色和纹理——将对抗性内容嵌入其中的技术。这种对抗性内容仅从特定视角可见。此外，我们还展示了DAGGER，这是一种直接扰动3D高斯分布而无需访问底层训练数据的定向对抗攻击，通过诸如投影梯度下降等方法欺骗多阶段物体检测器（例如Faster R-CNN）。这些攻击揭示了在3DGS中尚未被充分探索的安全漏洞，并为自主导航和其他安全关键型3DGS应用中的机器人学习引入了一个新的潜在威胁。
URL
https://arxiv.org/abs/2506.00280
