  The deep neural network is vulnerable to adversarial examples. Adding
imperceptible adversarial perturbations to images is enough to make them fail.
Most existing research focuses on attacking image classifiers or anchor-based
object detectors, but they generate globally perturbation on the whole image,
which is unnecessary. In our work, we leverage higher-level semantic
information to generate high aggressive local perturbations for anchor-free
object detectors. As a result, it is less computationally intensive and
achieves a higher black-box attack as well as transferring attack performance.
The adversarial examples generated by our method are not only capable of
attacking anchor-free object detectors, but also able to be transferred to
attack anchor-based object detector.
