  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. Code will be available at
https://github.com/jeongjin0/invisible-backdoor-object-detection
