针对自动驾驶车辆的交通标志上的不可见光学对抗条纹
Invisible Optical Adversarial Stripes on Traffic Sign against Autonomous Vehicles
AI总结：
本文提出利用LED和摄像头卷帘快门效应生成人眼不可见对抗条纹的GhostStripe攻击系统，可稳定误导自动驾驶交通标志识别，最高达94%的帧识别错误，并探讨了多层面防御对策。
摘要：
基于摄像头的计算机视觉对自动驾驶车辆的感知至关重要。本文提出一种攻击方法，利用发光二极管并借助摄像头的卷帘快门效应，在拍摄的图像中生成对抗条纹，以误导交通标志识别。该攻击具有隐蔽性，因为交通标志上的条纹对人眼不可见。为使攻击具备威胁性，识别结果需在连续图像帧上保持稳定。为此，我们设计并实现了GhostStripe攻击系统，该系统控制调制光发射的时序，以适配摄像头工作模式和受害车辆的运动。在真实测试平台上评估显示，当受害车辆经过该路段时，GhostStripe可在高达94%的帧中稳定地将交通标志识别结果欺骗为错误类别。在实际场景中，这种攻击效果可能诱使受害车辆发生危及生命的事故。我们还从摄像头传感器、感知模型和自动驾驶系统层面探讨了防御对策。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
