  Machine learning models can leak information regarding the dataset they have
trained. In this paper, we present the first membership inference attack
against black-boxed object detection models that determines whether the given
data records are used in the training. To attack the object detection model, we
devise a novel method named as called a canvas method, in which predicted
bounding boxes are drawn on an empty image for the attack model input. Based on
the experiments, we successfully reveal the membership status of privately
sensitive data trained using one-stage and two-stage detection models. We then
propose defense strategies and also conduct a transfer attack between the
models and datasets. Our results show that object detection models are also
vulnerable to inference attacks like other models.
