  Deep neural networks have been demonstrated to be vulnerable to adversarial
attacks: subtle perturbations can completely change the classification results.
Their vulnerability has led to a surge of research in this direction. However,
most works dedicated to attacking anchor-based object detection models. In this
work, we aim to present an effective and efficient algorithm to generate
adversarial examples to attack anchor-free object models based on two
approaches. First, we conduct category-wise instead of instance-wise attacks on
the object detectors. Second, we leverage the high-level semantic information
to generate the adversarial examples. Surprisingly, the generated adversarial
examples it not only able to effectively attack the targeted anchor-free object
detector but also to be transferred to attack other object detectors, even
anchor-based detectors such as Faster R-CNN.
