  Although there are a great number of adversarial attacks on deep learning
based classifiers, how to attack object detection systems has been rarely
studied. In this paper, we propose a Half-Neighbor Masked Projected Gradient
Descent (HNM-PGD) based attack, which can generate strong perturbation to fool
different kinds of detectors under strict constraints. We also applied the
proposed HNM-PGD attack in the CIKM 2020 AnalytiCup Competition, which was
ranked within the top 1% on the leaderboard. We release the code at
https://github.com/YanghaoZYH/HNM-PGD.

Computer Vision and Pattern Recognition;Machine Learning