  Model extraction increasingly attracts research attentions as keeping
commercial AI models private can retain a competitive advantage. In some
scenarios, AI models are trained proprietarily, where neither pre-trained
models nor sufficient in-distribution data is publicly available. Model
extraction attacks against these models are typically more devastating.
Therefore, in this paper, we empirically investigate the behaviors of model
extraction under such scenarios. We find the effectiveness of existing
techniques significantly affected by the absence of pre-trained models. In
addition, the impacts of the attacker's hyperparameters, e.g. model
architecture and optimizer, as well as the utilities of information retrieved
from queries, are counterintuitive. We provide some insights on explaining the
possible causes of these phenomena. With these observations, we formulate model
extraction attacks into an adaptive framework that captures these factors with
deep reinforcement learning. Experiments show that the proposed framework can
be used to improve existing techniques, and show that model extraction is still
possible in such strict scenarios. Our research can help system designers to
construct better defense strategies based on their scenarios.
