  Convolutional neural networks (CNNs) have demonstrated rapid progress and a
high level of success in object detection. However, recent evidence has
highlighted their vulnerability to adversarial attacks. These attacks are
calculated image perturbations or adversarial patches that result in object
misclassification or detection suppression. Traditional camouflage methods are
impractical when applied to disguise aircraft and other large mobile assets
from autonomous detection in intelligence, surveillance and reconnaissance
technologies and fifth generation missiles. In this paper we present a unique
method that produces imperceptible patches capable of camouflaging large
military assets from computer vision-enabled technologies. We developed these
patches by maximising object detection loss whilst limiting the patch's colour
perceptibility. This work also aims to further the understanding of adversarial
examples and their effects on object detection algorithms.
