  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 https://github.com/Wu-Shudeng/DPAttack.
