Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies
This addresses a critical security issue for military operations by providing a practical method to evade object detection in intelligence, surveillance, and reconnaissance technologies, representing an incremental advancement in adversarial attack applications.
The paper tackled the problem of camouflaging large military assets from computer vision-enabled technologies by developing imperceptible adversarial patches that maximize object detection loss while limiting color perceptibility, resulting in effective camouflage against autonomous detection systems.
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.