  Adversarial examples have gained tons of attention in recent years. Many
adversarial attacks have been proposed to attack image classifiers, but few
work shift attention to object detectors. In this paper, we propose Sparse
Adversarial Attack (SAA) which enables adversaries to perform effective evasion
attack on detectors with bounded \emph{l$_{0}$} norm perturbation. We select
the fragile position of the image and designed evasion loss function for the
task. Experiment results on YOLOv4 and FasterRCNN reveal the effectiveness of
our method. In addition, our SAA shows great transferability across different
detectors in the black-box attack setting. Codes are available at
\emph{https://github.com/THUrssq/Tianchi04}.
