Deep neural network model is the core technology for sensors of the autonomous driving platform to perceive the external environment. Recent research have shown that it has a certain vulnerability. The artificial designed adversarial examples can make the DNN model output the wrong results. These adversarial examples not only exist in the digital world, but also in the physical world. At present, research on autonomous driving platform mainly focus on attacking a single sensor. In this paper, we introduce Camdar‐adv, a method for generating image adversarial examples on three‐dimensional (3D) objects, which could potentially lunch a multisensor attack toward the autonomous driving platforms. Specifically, with objects that can attack LiDAR sensors, a geometric transformation can be used to project their shape onto the two‐dimensional plane. Adversarial perturbations against optical image sensor could be added to the surface of the adversarial 3D objects precisely without changing its geometry. Test results on the open‐source autonomous driving data set KITTI show that Camdar‐adv can generate adversarial samples for the state of the art object detection model. From a fixed viewpoint, our method can achieve an attack success rate over 99%.

中文翻译：

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Camdar-adv：在3D对象上生成对抗性补丁

深度神经网络模型是自动驾驶平台传感器感知外部环境的核心技术。最近的研究表明它具有一定的脆弱性。人工设计的对抗示例可能会使DNN模型输出错误的结果。这些对抗性例子不仅存在于数字世界中，而且存在于物理世界中。目前，对自动驾驶平台的研究主要集中在攻击单个传感器上。在本文中，我们介绍了Camdar-adv，这是一种在三维（3D）对象上生成图像对抗示例的方法，这可能会导致对自动驾驶平台的多传感器攻击。具体来说，对于可以攻击LiDAR传感器的物体，可以使用几何变换将其形状投影到二维平面上。可以将针对光学图像传感器的对抗性干扰精确地添加到对抗性3D对象的表面，而无需更改其几何形状。在开源自动驾驶数据集KITTI上的测试结果表明，Camdar-adv可以为最新的物体检测模型生成对抗性样本。从固定的角度来看，我们的方法可以实现超过99％的攻击成功率。