  We focus on the problem of black-box adversarial attacks, where the aim is to
generate adversarial examples using information limited to loss function
evaluations of input-output pairs. We use Bayesian optimization~(BO) to
specifically cater to scenarios involving low query budgets to develop query
efficient adversarial attacks. We alleviate the issues surrounding BO in
regards to optimizing high dimensional deep learning models by effective
dimension upsampling techniques. Our proposed approach achieves performance
comparable to the state of the art black-box adversarial attacks albeit with a
much lower average query count. In particular, in low query budget regimes, our
proposed method reduces the query count up to $80\%$ with respect to the state
of the art methods.
