  Deep neural networks (DNNs) have become essential for processing the vast
amounts of aerial imagery collected using earth-observing satellite platforms.
However, DNNs are vulnerable towards adversarial examples, and it is expected
that this weakness also plagues DNNs for aerial imagery. In this work, we
demonstrate one of the first efforts on physical adversarial attacks on aerial
imagery, whereby adversarial patches were optimised, fabricated and installed
on or near target objects (cars) to significantly reduce the efficacy of an
object detector applied on overhead images. Physical adversarial attacks on
aerial images, particularly those captured from satellite platforms, are
challenged by atmospheric factors (lighting, weather, seasons) and the distance
between the observer and target. To investigate the effects of these
challenges, we devised novel experiments and metrics to evaluate the efficacy
of physical adversarial attacks against object detectors in aerial scenes. Our
results indicate the palpable threat posed by physical adversarial attacks
towards DNNs for processing satellite imagery.
