  Machine Learning models, extensively used for various multimedia
applications, are offered to users as a blackbox service on the Cloud on a
pay-per-query basis. Such blackbox models are commercially valuable to
adversaries, making them vulnerable to extraction attacks to reverse engineer
the proprietary model thereby violating the model privacy and Intellectual
Property. Here, the adversary first extracts the model architecture or
hyperparameters through side channel leakage, followed by stealing the
functionality of the target model by training the reconstructed architecture on
a synthetic dataset. While the attacks proposed in literature are empirical,
there is a need for a theoretical framework to measure the information leaked
under such extraction attacks. To this extent, in this work, we propose a novel
probabilistic framework, Airavata, to estimate the information leakage in such
model extraction attacks. This framework captures the fact that extracting the
exact target model is difficult due to experimental uncertainty while inferring
model hyperparameters and stochastic nature of training to steal the target
model functionality. Specifically, we use Bayesian Networks to capture
uncertainty in estimating the target model under various extraction attacks
based on the subjective notion of probability. We validate the proposed
framework under different adversary assumptions commonly adopted in literature
to reason about the attack efficacy. This provides a practical tool to infer
actionable details about extracting blackbox models and help identify the best
attack combination which maximises the knowledge extracted (or information
leaked) from the target model.
