  Object detection forms a key component in Unmanned Aerial Vehicles (UAVs) for
completing high-level tasks that depend on the awareness of objects on the
ground from an aerial perspective. In that scenario, adversarial patch attacks
on an onboard object detector can severely impair the performance of upstream
tasks. This paper proposes a novel model-agnostic defense mechanism against the
threat of adversarial patch attacks in the context of UAV-based object
detection. We formulate adversarial patch defense as an occlusion removal task.
The proposed defense method can neutralize adversarial patches located on
objects of interest, without exposure to adversarial patches during training.
Our lightweight single-stage defense approach allows us to maintain a
model-agnostic nature, that once deployed does not require to be updated in
response to changes in the object detection pipeline. The evaluations in
digital and physical domains show the feasibility of our method for deployment
in UAV object detection pipelines, by significantly decreasing the Attack
Success Ratio without incurring significant processing costs. As a result, the
proposed defense solution can improve the reliability of object detection for
UAVs.
