论文标题
影子捕捉者：看阴影以检测自动驾驶汽车中的幽灵对象3D传感
Shadow-Catcher: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing
论文作者
论文摘要
LIDAR驱动的3D传感使新一代的车辆能够达到高级情况意识。但是，最近的作品表明，物理对手可以欺骗激光雷达信号，并欺骗3D对象检测器以错误地检测“鬼魂”对象。现有的防御能力是不切实际的，要么仅专注于车辆。不幸的是，欺骗较小的物体（例如行人和骑自行车的人）更容易，但是很难防御并且可能对安全性产生更严重的影响。为了解决这一差距，我们介绍了Shadow-Catcher，这是一组在端到端原型中体现的新技术，以检测3D检测器上的大型和小幽灵对象攻击。我们表征了一个新的具有语义上有意义的物理不变（3D阴影），该物理不变（3D阴影）利用了阴影捕捉器来验证对象。我们在Kitti数据集上的评估表明，Shadow Catcher在识别车辆，行人和骑自行车者的异常阴影方面始终达到94％以上的精度，而对于针对国防系统的新型强大“无效”攻击仍然是强大的。影子捕捉器可以实现实时检测，平均仅需要0.003s-0.021在商品硬件上的3D点云中处理一个对象，并且与先前的工作相比实现了2.17倍的速度
LiDAR-driven 3D sensing allows new generations of vehicles to achieve advanced levels of situation awareness. However, recent works have demonstrated that physical adversaries can spoof LiDAR return signals and deceive 3D object detectors to erroneously detect "ghost" objects. Existing defenses are either impractical or focus only on vehicles. Unfortunately, it is easier to spoof smaller objects such as pedestrians and cyclists, but harder to defend against and can have worse safety implications. To address this gap, we introduce Shadow-Catcher, a set of new techniques embodied in an end-to-end prototype to detect both large and small ghost object attacks on 3D detectors. We characterize a new semantically meaningful physical invariant (3D shadows) which Shadow-Catcher leverages for validating objects. Our evaluation on the KITTI dataset shows that Shadow-Catcher consistently achieves more than 94% accuracy in identifying anomalous shadows for vehicles, pedestrians, and cyclists, while it remains robust to a novel class of strong "invalidation" attacks targeting the defense system. Shadow-Catcher can achieve real-time detection, requiring only between 0.003s-0.021s on average to process an object in a 3D point cloud on commodity hardware and achieves a 2.17x speedup compared to prior work
