  Deep learning models excel in various computer vision tasks but are
susceptible to adversarial examples-subtle perturbations in input data that
lead to incorrect predictions. This vulnerability poses significant risks in
safety-critical applications such as autonomous vehicles, security
surveillance, and aircraft health monitoring. While numerous surveys focus on
adversarial attacks in image classification, the literature on such attacks in
object detection is limited. This paper offers a comprehensive taxonomy of
adversarial attacks specific to object detection, reviews existing adversarial
robustness evaluation metrics, and systematically assesses open-source attack
methods and model robustness. Key observations are provided to enhance the
understanding of attack effectiveness and corresponding countermeasures.
Additionally, we identify crucial research challenges to guide future efforts
in securing automated object detection systems.
