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.

本研究针对目标检测领域中对抗样本的脆弱性这一问题进行了深入的调查与评估，填补了现有文献中的空白。论文提出了一种全面的对抗攻击分类法，并系统评估了现有的攻击方法与模型的鲁棒性，为提高目标检测系统的安全性提供了关键见解和研究挑战。

目标检测中的对抗攻击调查与评估