Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors
This work addresses the need for more effective physical attacks against vehicle detectors, which is an incremental improvement for security testing and adversarial robustness in autonomous systems.
The paper tackled the problem of generating adversarial camouflage for vehicles that is robust across different weather conditions and accurately maps to target vehicles, resulting in RAUCA outperforming existing methods on six object detectors in both simulation and real-world settings.
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. 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, End-to-End Neural Renderer Plus (E2E-NRP), which can accurately optimize and 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 E2E-NRP to enhance the attack robustness. Experimental results on six popular object detectors show that RAUCA-final outperforms existing methods in both simulation and real-world settings.