EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in
  UAV
Huiming Sun Jiacheng Guo Zibo Meng Tianyun Zhang Jianwu Fang Yuewei Lin Hongkai Yu
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.