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

2024/02/24 by Jiawei Zhou, Linye Lyu, Zhou, Jiawei +5 · 5 citations
Computer Science · Engineering · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Infrared Target Detection Methodologies #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.2402.15853

openalex publication_date 2024/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks
• ComplicitSplat: Downstream Models are Vulnerable to Blackbox Attacks by 3D Gaussian Splat Camouflages
• Physical Adversarial Camouflage through Gradient Calibration and Regularization
• 3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving
• 3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation

Related

• Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors
• CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors
• Flexible Physical Camouflage Generation Based on a Differential Approach
• ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion
• DTA: Physical Camouflage Attacks using Differentiable Transformation Network