  We focus on the problem of black-box adversarial attacks, where the aim is to
generate adversarial examples for deep learning models solely based on
information limited to output label~(hard label) to a queried data input. We
propose a simple and efficient Bayesian Optimization~(BO) based approach for
developing black-box adversarial attacks. Issues with BO's performance in high
dimensions are avoided by searching for adversarial examples in a structured
low-dimensional subspace. We demonstrate the efficacy of our proposed attack
method by evaluating both $\ell_\infty$ and $\ell_2$ norm constrained
untargeted and targeted hard label black-box attacks on three standard datasets
- MNIST, CIFAR-10 and ImageNet. Our proposed approach consistently achieves 2x
to 10x higher attack success rate while requiring 10x to 20x fewer queries
compared to the current state-of-the-art black-box adversarial attacks.
