  Deep neural networks have been demonstrated to be vulnerable to adversarial
attacks: subtle perturbation can completely change prediction result. The
vulnerability has led to a surge of research in this direction, including
adversarial attacks on object detection networks. However, previous studies are
dedicated to attacking anchor-based object detectors. In this paper, we present
the first adversarial attack on anchor-free object detectors. It conducts
category-wise, instead of previously instance-wise, attacks on object
detectors, and leverages high-level semantic information to efficiently
generate transferable adversarial examples, which can also be transferred to
attack other object detectors, even anchor-based detectors such as Faster
R-CNN. Experimental results on two benchmark datasets demonstrate that our
proposed method achieves state-of-the-art performance and transferability.
