Recently, deep neural networks (DNNs) have been widely and successfully used
in Object Detection, e.g. Faster RCNN, YOLO, CenterNet. However, recent studies
have shown that DNNs are vulnerable to adversarial attacks. Adversarial attacks
against object detection can be divided into two categories, whole-pixel
attacks and patch attacks. While these attacks add perturbations to a large
number of pixels in images, we proposed a diffused patch attack
(\textbf{DPAttack}) to successfully fool object detectors by diffused patches
of asteroid-shaped or grid-shape, which only change a small number of pixels.
Experiments show that our DPAttack can successfully fool most object detectors
with diffused patches and we get the second place in the Alibaba Tianchi
competition: Alibaba-Tsinghua Adversarial Challenge on Object Detection. Our
code can be obtained from this https URL

本研究提出了一种基于扩散贴片攻击方法（DPAttack）的物体检测对抗攻击，通过对小部分像素进行扰动，成功地欺骗了大多数物体检测器，并在 Alibaba-Tsinghua 对抗性目标检测挑战中获得了第二名。