From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories

Abstract

Deep models' feature learning capabilities have gained traction in recent years, driving significant progress in various Artificial Intelligence (AI) domains. The use of Deep Neural Networks (DNNs) has expanded the scope of Computer Vision (CV) and revealed their vulnerability to deliberate adversarial attacks. These attacks involve the careful introduction of perturbations crafted through complex optimization problems. Exploiting vulnerabilities in advanced deep neural network algorithms present security concerns, particularly in practical applications with high stakes like unmanned aerial vehicles (UAVs) and satellite imagery in computer vision. Adversarial attacks, both in digital and physical dimensions, pose a serious threat in the field. This research provides a comprehensive examination of state-of-the-art adversarial attacks specific to aerial imagery using autonomous platforms such as UAVs and satellites. This review covers fundamental concepts, techniques, and the latest advancements, identifying research gaps and suggesting future directions. It aims to deepen researchers' understanding of the challenges and threats related to adversarial attacks on aerial imagery, serving as a valuable resource to guide future research and enhance the security of computer vision systems in aerial environments.