In the last few years, artificial intelligence has made great strides in various domains including object detection which led to key components in every-day applications like self driving cars and security systems. But, the robustness and security of these models have become a cause for concern, especially with the arrival of sophisticated attack vectors like backdoor attacks. In this work, we examine an implementation of a backdoor attack on object detection models with emphasis on Faster R-CNN models which have been thoroughly used and researched in the field. We compare the current state of artificial intelligence with cryptography, emphasizing the need for rigorous defenses against such attacks. The research of this work will go into details on four different types of backdoor attacks: OGA (object generation attack), RMA (regional misclassification attack), GMA (global misclassification attack), and ODA (object dissapearance attack). Generating posioned datasets and training a Faster R-CNN model on these datasets, we will analyze the impact of such attacks. The efficiency of these attacks will be evaluated using metrics such as mAP (mean average precision) and ASR (attack success rate). These metrics will emphasize the vulnerability of object detection models to backdoor attacks.