Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies

TL;DR

This paper introduces imperceptible adversarial patches designed to camouflage large military assets from computer vision detection, enhancing understanding of adversarial effects on object detection systems.

Contribution

The paper presents a novel method for creating imperceptible patches that effectively hide large military assets from AI-based detection systems.

Findings

Patches successfully camouflage military assets from detection.

Maximizing detection loss while limiting perceptibility is effective.

Enhances understanding of adversarial impacts on object detection.

Abstract

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.