  Machine learning as a Service (MLaaS) allows users to query the machine
learning model in an API manner, which provides an opportunity for users to
enjoy the benefits brought by the high-performance model trained on valuable
data. This interface boosts the proliferation of machine learning based
applications, while on the other hand, it introduces the attack surface for
model stealing attacks. Existing model stealing attacks have relaxed their
attack assumptions to the data-free setting, while keeping the effectiveness.
However, these methods are complex and consist of several components, which
obscure the core on which the attack really depends. In this paper, we revisit
the model stealing problem from a diversity perspective and demonstrate that
keeping the generated data samples more diverse across all the classes is the
critical point for improving the attack performance. Based on this conjecture,
we provide a simplified attack framework. We empirically signify our conjecture
by evaluating the effectiveness of our attack, and experimental results show
that our approach is able to achieve comparable or even better performance
compared with the state-of-the-art method. Furthermore, benefiting from the
absence of redundant components, our method demonstrates its advantages in
attack efficiency and query budget.
