  Intelligent robots rely on object detection models to perceive the
environment. Following advances in deep learning security it has been revealed
that object detection models are vulnerable to adversarial attacks. However,
prior research primarily focuses on attacking static images or offline videos.
Therefore, it is still unclear if such attacks could jeopardize real-world
robotic applications in dynamic environments. This paper bridges this gap by
presenting the first real-time online attack against object detection models.
We devise three attacks that fabricate bounding boxes for nonexistent objects
at desired locations. The attacks achieve a success rate of about 90% within
about 20 iterations. The demo video is available at
https://youtu.be/zJZ1aNlXsMU.
