  Deep neural networks have been widely used in many computer vision tasks.
However, it is proved that they are susceptible to small, imperceptible
perturbations added to the input. Inputs with elaborately designed
perturbations that can fool deep learning models are called adversarial
examples, and they have drawn great concerns about the safety of deep neural
networks. Object detection algorithms are designed to locate and classify
objects in images or videos and they are the core of many computer vision
tasks, which have great research value and wide applications. In this paper, we
focus on adversarial attack on some state-of-the-art object detection models.
As a practical alternative, we use adversarial patches for the attack. Two
adversarial patch generation algorithms have been proposed: the heatmap-based
algorithm and the consensus-based algorithm. The experiment results have shown
that the proposed methods are highly effective, transferable and generic.
Additionally, we have applied the proposed methods to competition "Adversarial
Challenge on Object Detection" that is organized by Alibaba on the Tianchi
platform and won top 7 in 1701 teams. Code is available at:
https://github.com/FenHua/DetDak
