  Artificial neural networks (ANNs) have gained significant popularity in the
last decade for solving narrow AI problems in domains such as healthcare,
transportation, and defense. As ANNs become more ubiquitous, it is imperative
to understand their associated safety, security, and privacy vulnerabilities.
Recently, it has been shown that ANNs are susceptible to a number of
adversarial evasion attacks--inputs that cause the ANN to make high-confidence
misclassifications despite being almost indistinguishable from the data used to
train and test the network. This work explores to what degree finding these
examples maybe aided by using side-channel information, specifically switching
power consumption, of hardware implementations of ANNs. A black-box threat
scenario is assumed, where an attacker has access to the ANN hardware's input,
outputs, and topology, but the trained model parameters are unknown. Then, a
surrogate model is trained to have similar functional (i.e. input-output
mapping) and switching power characteristics as the oracle (black-box) model.
Our results indicate that the inclusion of power consumption data increases the
fidelity of the model extraction by up to 30 percent based on a mean square
error comparison of the oracle and surrogate weights. However, transferability
of adversarial examples from the surrogate to the oracle model was not
significantly affected.
