  Person detection has attracted great attention in the computer vision area
and is an imperative element in human-centric computer vision. Although the
predictive performances of person detection networks have been improved
dramatically, they are vulnerable to adversarial patch attacks. Changing the
pixels in a restricted region can easily fool the person detection network in
safety-critical applications such as autonomous driving and security systems.
Despite the necessity of countering adversarial patch attacks, very few efforts
have been dedicated to defending person detection against adversarial patch
attack. In this paper, we propose a novel defense strategy that defends against
an adversarial patch attack by optimizing a defensive frame for person
detection. The defensive frame alleviates the effect of the adversarial patch
while maintaining person detection performance with clean person. The proposed
defensive frame in the person detection is generated with a competitive
learning algorithm which makes an iterative competition between detection
threatening module and detection shielding module in person detection.
Comprehensive experimental results demonstrate that the proposed method
effectively defends person detection against adversarial patch attacks.
