Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks. Previous physical attacks against MDE models rely on 2D adversarial patches, so they only affect a small, localized region in the MDE map but fail under various viewpoints. To address these limitations, we propose 3D Depth Fool (3D$^2$Fool), the first 3D texture-based adversarial attack against MDE models. 3D$^2$Fool is specifically optimized to generate 3D adversarial textures agnostic to model types of vehicles and to have improved robustness in bad weather conditions, such as rain and fog. Experimental results validate the superior performance of our 3D$^2$Fool across various scenarios, including vehicles, MDE models, weather conditions, and viewpoints. Real-world experiments with printed 3D textures on physical vehicle models further demonstrate that our 3D$^2$Fool can cause an MDE error of over 10 meters.

翻译：基于深度学习的单目深度估计（MDE）在自动驾驶中广泛应用，但已知易受对抗攻击影响。以往针对MDE模型的物理攻击依赖2D对抗性贴片，因此仅影响MDE地图中一个较小的局部区域，且在不同视角下会失效。为解决这些局限性，我们提出3D深度欺骗（3D$^2$Fool），这是首个针对MDE模型的基于3D纹理的对抗攻击。3D$^2$Fool经过专门优化，可生成与车辆模型类型无关的3D对抗纹理，并在雨天和雾天等恶劣天气条件下具有更强的鲁棒性。实验结果验证了我们的3D$^2$Fool在包括车辆、MDE模型、天气条件和视角在内的多种场景下的优越性能。在物理车辆模型上使用打印的3D纹理进行的真实世界实验进一步表明，我们的3D$^2$Fool可导致超过10米的MDE误差。