  Deep neural networks (DNNs) are vulnerable to adversarial attacks. In
particular, object detectors may be attacked by applying a particular
adversarial patch to the image. However, because the patch shrinks during
preprocessing, most existing approaches that employ adversarial patches to
attack object detectors would diminish the attack success rate on small and
medium targets. This paper proposes a Frequency Module(FRAN), a
frequency-domain attention module for guiding patch generation. This is the
first study to introduce frequency domain attention to optimize the attack
capabilities of adversarial patches. Our method increases the attack success
rates of small and medium targets by 4.18% and 3.89%, respectively, over the
state-of-the-art attack method for fooling the human detector while assaulting
YOLOv3 without reducing the attack success rate of big targets.
