We consider the zeroth-order optimization problem in the huge-scale setting,
where the dimension of the problem is so large that performing even basic
vector operations on the decision variables is infeasible. In this paper, we
propose a novel algorithm, coined ZO-BCD, that exhibits favorable overall query
complexity and has a much smaller per-iteration computational complexity. In
addition, we discuss how the memory footprint of ZO-BCD can be reduced even
further by the clever use of circulant measurement matrices. As an application
of our new method, we propose the idea of crafting adversarial attacks on
neural network based classifiers in a wavelet domain, which can result in
problem dimensions of over 1.7 million. In particular, we show that crafting
adversarial examples to audio classifiers in a wavelet domain can achieve the
state-of-the-art attack success rate of 97.9%.

本研究提出了一种名为 ZO-BCD 的零阶优化算法，可在大规模环境下完成优化问题的求解，并在小迭代计算复杂度下具有较佳的查询复杂度和更小的内存占用，同时将其应用在音频分类器中，实现了在小波域的对抗攻击效果。