Vehicle detection in Unmanned Aerial Vehicle (UAV) captured images has wide applications in aerial photography and remote sensing. There are many public benchmark datasets proposed for the vehicle detection and tracking in UAV images. Recent studies show that adding an adversarial patch on objects can fool the well-trained deep neural networks based object detectors, posing security concerns to the downstream tasks. However, the current public UAV datasets might ignore the diverse altitudes, vehicle attributes, fine-grained instance-level annotation in mostly side view with blurred vehicle roof, so none of them is good to study the adversarial patch based vehicle detection attack problem. In this paper, we propose a new dataset named EVD4UAV as an altitude-sensitive benchmark to evade vehicle detection in UAV with 6,284 images and 90,886 fine-grained annotated vehicles. The EVD4UAV dataset has diverse altitudes (50m, 70m, 90m), vehicle attributes (color, type), fine-grained annotation (horizontal and rotated bounding boxes, instance-level mask) in top view with clear vehicle roof. One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV. The experimental results show that these representative attack methods could not achieve the robust altitude-insensitive attack performance.

翻译：无人机航拍图像中的车辆检测在航空摄影和遥感领域具有广泛应用。目前已有多个公开基准数据集用于无人机图像中的车辆检测与跟踪。最新研究表明，在目标上添加对抗补丁可欺骗基于深度神经网络的检测器，对下游任务构成安全威胁。然而，现有公开无人机数据集普遍存在高度多样性不足、车辆属性标注缺失、主要提供侧视角度且车顶模糊导致无法进行细粒度实例级标注等问题，均不适合研究基于对抗补丁的车辆检测攻击问题。本文提出名为EVD4UAV的新数据集，作为面向无人机车辆检测逃避的高度敏感基准，包含6,284张图像和90,886个细粒度标注车辆。该数据集具有俯视角度下清晰车顶特征，涵盖多高度层级（50米、70米、90米）、车辆属性（颜色、类型）及细粒度标注（水平/旋转边界框、实例级掩膜）。我们采用一种白盒和两种黑盒补丁攻击方法，对三种经典深度神经网络检测器进行攻击测试。实验结果表明，现有代表性攻击方法无法实现鲁棒的高度不敏感攻击性能。