RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via
  Robust and Accurate Camouflage Generation
Jiawei Zhou Linye Lyu Daojing He Yu Li
Adversarial camouflage is a widely used physical attack against vehicle
detectors for its superiority in multi-view attack performance. One promising
approach involves using differentiable neural renderers to facilitate
adversarial camouflage optimization through gradient back-propagation. However,
existing methods often struggle to capture environmental characteristics during
the rendering process or produce adversarial textures that can precisely map to
the target vehicle, resulting in suboptimal attack performance. Moreover, these
approaches neglect diverse weather conditions, reducing the efficacy of
generated camouflage across varying weather scenarios. To tackle these
challenges, we propose a robust and accurate camouflage generation method,
namely RAUCA. The core of RAUCA is a novel neural rendering component, Neural
Renderer Plus (NRP), which can accurately project vehicle textures and render
images with environmental characteristics such as lighting and weather. In
addition, we integrate a multi-weather dataset for camouflage generation,
leveraging the NRP to enhance the attack robustness. Experimental results on
six popular object detectors show that RAUCA consistently outperforms existing
methods in both simulation and real-world settings.