We propose an intriguingly simple method for the construction of adversarial
images in the black-box setting. In constrast to the white-box scenario,
constructing black-box adversarial images has the additional constraint on
query budget, and efficient attacks remain an open problem to date. With only
the mild assumption of continuous-valued confidence scores, our highly
query-efficient algorithm utilizes the following simple iterative principle: we
randomly sample a vector from a predefined orthonormal basis and either add or
subtract it to the target image. Despite its simplicity, the proposed method
can be used for both untargeted and targeted attacks -- resulting in previously
unprecedented query efficiency in both settings. We demonstrate the efficacy
and efficiency of our algorithm on several real world settings including the
Google Cloud Vision API. We argue that our proposed algorithm should serve as a
strong baseline for future black-box attacks, in particular because it is
extremely fast and its implementation requires less than 20 lines of PyTorch
code.

本文介绍了一个基于连续数值置信度分数的、高效构建黑盒中敌对图像的简单迭代算法，并且该算法同样适用于有目标和无目标攻击。作者使用少于 20 行的 PyTorch 代码在 Google Cloud Vision API 中展示了该算法的高效和有效性。