基于视觉的汽车检测系统物理对抗攻击  

Physical adversarial attack on vehicle detection systems

作　　者：刘育秋 唐亮[1] 王宁珍 LIU Yuqiu;TANG Liang;WANG Ningzhen(School of Technology,Bejing Forestry University,Beijing 10083,China)

机构地区：[1]北京林业大学工学院,北京100083

出　　处：《汽车安全与节能学报》2025年第1期50-56,共7页Journal of Automotive Safety and Energy

基　　金：汽车零部件先进制造技术教育部重点实验室开放课题基金(2021KLMT05)。

摘　　要：为保护个人车辆的隐私信息,实现汽车伪装,对基于视觉的汽车检测系统进行物理对抗攻击。在3D对抗攻击框架的基础上,对攻击算法的目标函数进行优化设计,提升3D对抗纹理在多视角多场景设定下的攻击效果,设计针对天气变化的带权分层颜色映射网络,使对抗纹理能够在训练流程中表达在天气参数下的颜色反应,从而进一步增强攻击在物理世界实现的鲁棒性。进行了数字和物理实验。结果表明:基于视觉的汽车检测系统在本算法攻击下检测的平均召回率降低了49.4%。从而,本算法优化出的对抗纹理具备的物理世界可实现性,能够在物理世界实现使检测系统的准确率下降至少38.7%。A camouflage method was developed for conducting physical adversarial attacks on vision-based vehicle detection systems to protect the privacy of personal vehicles.An objective function of the attack algorithm was optimized based on a 3D adversarial attack framework to enhance the effectiveness of 3D adversarial textures under multi-view and multi-scene settings.A weather-adaptive weighted hierarchical color mapping network was designed to enable adversarial textures to respond to weather parameters during the training process,further improving the robustness of physical-world attacks.Digital and physical experiments were conducted.The results show that the proposed algorithm reduces the average recall rate of detection by 49.4%.Therefore,the optimized adversarial textures demonstrate physical-world feasibility,achieving at least a 38.7%reduction in detection accuracy in real-world scenarios.

关 键 词：汽车检测系统 物理对抗攻击 深度学习 计算机视觉 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]