Deep learning models have achieved unprecedented performance in the domain of object detection, resulting in breakthroughs in areas such as autonomous driving and security. However, deep learning models are vulnerable to backdoor attacks. These attacks prompt models to behave similarly to standard models without a trigger; however, they act maliciously upon detecting a predefined trigger. Despite extensive research on backdoor attacks in image classification, their application to object detection remains relatively underexplored. Given the widespread application of object detection in critical real-world scenarios, the sensitivity and potential impact of these vulnerabilities cannot be overstated. In this study, we propose an effective invisible backdoor attack on object detection utilizing a mask-based approach. Three distinct attack scenarios were explored for object detection: object disappearance, object misclassification, and object generation attack. Through extensive experiments, we comprehensively examined the effectiveness of these attacks and tested certain defense methods to determine effective countermeasures.

翻译：深度学习模型在目标检测领域取得了前所未有的性能，推动了自动驾驶和安全等领域的突破性进展。然而，深度学习模型容易受到后门攻击。这类攻击使得模型在无触发条件时表现与标准模型相似，但在检测到预定义的触发条件时则执行恶意行为。尽管针对图像分类的后门攻击已有广泛研究，但其在目标检测中的应用仍相对不足。鉴于目标检测在关键现实场景中的广泛应用，这些漏洞的敏感性和潜在影响不容忽视。在本研究中，我们提出了一种基于掩码的有效不可见后门攻击方法，应用于目标检测。针对目标检测探索了三种不同的攻击场景：目标消失、目标误分类和目标生成攻击。通过大量实验，我们全面检验了这些攻击的有效性，并测试了部分防御方法以确定有效的应对措施。