Unlike the white-box counterparts that are widely studied and readily accessible, adversarial examples in black-box settings are generally more Herculean on account of the difficulty of estimating gradients. Many methods achieve the task by issuing numerous queries to target classification systems, which makes the whole procedure costly and suspicious to the systems. In this paper, we aim at reducing the query complexity of black-box attacks in this category. We propose to exploit gradients of a few reference models which arguably span some promising search subspaces. Experimental results show that, in comparison with the state-of-the-arts, our method can gain up to 2x and 4x reductions in the requisite mean and medium numbers of queries with much lower failure rates even if the reference models are trained on a small and inadequate dataset disjoint to the one for training the victim model. Code and models for reproducing our results will be made publicly available.

本文介绍如何在黑箱攻击中利用参考模型的梯度来降低查询复杂度，提高黑箱攻击的效率和成功率，实验结果表明，与现有技术相比，本文提出的方法可以在查询数量上获得2x到4x的降低，并且具有更低的失败率。

子空间攻击：利用有前途的子空间进行查询高效黑盒攻击