The widespread use of deep learning technology across various industries has
made deep neural network models highly valuable and, as a result, attractive
targets for potential attackers. Model extraction attacks, particularly
query-based model extraction attacks, allow attackers to replicate a substitute
model with comparable functionality to the victim model and present a
significant threat to the confidentiality and security of MLaaS platforms.
While many studies have explored threats of model extraction attacks against
classification models in recent years, object detection models, which are more
frequently used in real-world scenarios, have received less attention. In this
paper, we investigate the challenges and feasibility of query-based model
extraction attacks against object detection models and propose an effective
attack method called MEAOD. It selects samples from the attacker-possessed
dataset to construct an efficient query dataset using active learning and
enhances the categories with insufficient objects. We additionally improve the
extraction effectiveness by updating the annotations of the query dataset.
According to our gray-box and black-box scenarios experiments, we achieve an
extraction performance of over 70% under the given condition of a 10k query
budget.

对目标检测模型进行基于查询的模型提取攻击的挑战和可行性进行研究，提出了一种名为 MEAOD 的有效攻击方法，通过主动学习从攻击者拥有的数据集中选择样本构建高效的查询数据集，并通过更新查询数据集的标注来提高提取效果，在 10k 查询预算下，达到超过 70% 的提取性能。