Camera-based computer vision is essential to autonomous vehicle's perception. This paper presents an attack that uses light-emitting diodes and exploits the camera's rolling shutter effect to create adversarial stripes in the captured images to mislead traffic sign recognition. The attack is stealthy because the stripes on the traffic sign are invisible to human. For the attack to be threatening, the recognition results need to be stable over consecutive image frames. To achieve this, we design and implement GhostStripe, an attack system that controls the timing of the modulated light emission to adapt to camera operations and victim vehicle movements. Evaluated on real testbeds, GhostStripe can stably spoof the traffic sign recognition results for up to 94\% of frames to a wrong class when the victim vehicle passes the road section. In reality, such attack effect may fool victim vehicles into life-threatening incidents. We discuss the countermeasures at the levels of camera sensor, perception model, and autonomous driving system.

通过利用发光二极管并利用相机的卷帘效应，在捕捉图像中创建对抗条纹以误导交通标志识别，本文介绍了一种攻击方法。该攻击具有隐秘性，因为交通标志上的条纹对人眼是不可见的。为了使攻击具备威胁性，识别结果需要在连续图像帧上保持稳定。为了实现这一点，我们设计并实现了GhostStripe（幽灵条纹），一种攻击系统，其控制调制光发射的时间以适应相机操作和受害车辆的移动。在真实的测试环境中评估，当受害车辆经过道路时，GhostStripe可以稳定地欺骗交通标志识别结果，将识别错误的类别达到94％。在现实中，这种攻击效果可能会导致使受害车辆卷入危及生命的事件。我们讨论了在相机传感器、感知模型和自动驾驶系统层面上的对策。

自动驾驶车辆上的隐形光学对抗条纹交通标志