提出RAUCA方法，提升对抗伪装在复杂环境下的鲁棒性与精度。
Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors
- 设计端到端神经渲染器E2E-NRP，精准融合光照天气等环境特征
- 在六种检测器上实测，仿真与真实场景均优于现有方法
- 适配多天气数据集，适用于真实道路复杂环境的对抗攻击
对抗伪装是物理攻击车辆检测器的有效手段，尤其在多视角攻击中表现优异。现有方法常因无法准确捕捉渲染过程中的环境特征，或生成难以精确贴合目标车辆的对抗纹理，且忽略不同天气条件，导致攻击效果随环境变化而下降。为此，本文提出鲁棒且精确的伪装生成方法RAUCA，核心为新型神经渲染组件——端到端神经渲染器增强版（E2E-NRP），可精准优化并投影车辆纹理，生成包含光照、天气等环境特征的图像。同时引入多天气数据集，结合E2E-NRP提升攻击在不同天气下的鲁棒性。在六种主流目标检测器上的实验表明，RAUCA-final在仿真与真实场景中均显著优于现有方法。
原文摘要 · Abstract (English)
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.
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