  We present a method for adversarial input generation against black box models
for reading comprehension based question answering. Our approach is composed of
two steps. First, we approximate a victim black box model via model extraction
(Krishna et al., 2020). Second, we use our own white box method to generate
input perturbations that cause the approximate model to fail. These perturbed
inputs are used against the victim. In experiments we find that our method
improves on the efficacy of the AddAny---a white box attack---performed on the
approximate model by 25% F1, and the AddSent attack---a black box attack---by
11% F1 (Jia and Liang, 2017).
