Decision-based black-box attacks often necessitate a large number of queries
to craft an adversarial example. Moreover, decision-based attacks based on
querying boundary points in the estimated normal vector direction often suffer
from inefficiency and convergence issues. In this paper, we propose a novel
query-efficient curvature-aware geometric decision-based black-box attack
(CGBA) that conducts boundary search along a semicircular path on a restricted
2D plane to ensure finding a boundary point successfully irrespective of the
boundary curvature. While the proposed CGBA attack can work effectively for an
arbitrary decision boundary, it is particularly efficient in exploiting the low
curvature to craft high-quality adversarial examples, which is widely seen and
experimentally verified in commonly used classifiers under non-targeted
attacks. In contrast, the decision boundaries often exhibit higher curvature
under targeted attacks. Thus, we develop a new query-efficient variant, CGBA-H,
that is adapted for the targeted attack. In addition, we further design an
algorithm to obtain a better initial boundary point at the expense of some
extra queries, which considerably enhances the performance of the targeted
attack. Extensive experiments are conducted to evaluate the performance of our
proposed methods against some well-known classifiers on the ImageNet and
CIFAR10 datasets, demonstrating the superiority of CGBA and CGBA-H over
state-of-the-art non-targeted and targeted attacks, respectively. The source
code is available at this https URL

提出了一种新的查询高效的曲率感知几何决策黑盒攻击方法，使用半圆路径在受限的 2D 平面上进行边界搜索，有效地利用较低曲率来构造高质量的对抗样本，相比最先进的非目标和目标攻击方法显示出更优越的性能。