RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation

TL;DR

RAUCA introduces a neural rendering-based physical adversarial attack that generates robust vehicle camouflage effective across diverse weather conditions, significantly improving attack success on vehicle detectors.

Contribution

The paper proposes RAUCA, a novel camouflage generation method using Neural Renderer Plus to improve environmental accuracy and robustness in physical adversarial attacks.

Findings

Outperforms existing methods in simulation and real-world tests

Effective across multiple weather conditions

Enhances attack robustness and accuracy

Abstract

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.