ES Attack: Model Stealing Against Deep Neural Networks Without Data Hurdles

Deep neural networks (DNNs) have become the essential components for various commercialized machine learning services, such as Machine Learning as a Service (MLaaS). Recent studies show that machine learning services face severe privacy threats - well-trained DNNs owned by MLaaS providers can be stolen through public APIs, namely model stealing attacks. However, most existing works undervalued the impact of such attacks, where a successful attack has to acquire confidential training data or auxiliary data regarding the victim DNN. In this paper, we propose
ES Attack
, a novel model stealing attack without any data hurdles. By using heuristically generated synthetic data,
ES Attack
iteratively trains a substitute model and eventually achieves a functionally equivalent copy of the victim DNN. The experimental results reveal the severity of
ES Attack
: i)
ES Attack
successfully steals the victim model without data hurdles, and
ES Attack
even outperforms most existing model stealing attacks using auxiliary data in terms of model accuracy; ii) most countermeasures are ineffective in defending
ES Attack
; iii)
ES Attack
facilitates further attacks relying on the stolen model.