  In order to be applicable in real-world scenario, Boundary Attacks (BAs) were
proposed and ensured one hundred percent attack success rate with only decision
information. However, existing BA methods craft adversarial examples by
leveraging a simple random sampling (SRS) to estimate the gradient, consuming a
large number of model queries. To overcome the drawback of SRS, this paper
proposes a Latin Hypercube Sampling based Boundary Attack (LHS-BA) to save
query budget. Compared with SRS, LHS has better uniformity under the same
limited number of random samples. Therefore, the average on these random
samples is closer to the true gradient than that estimated by SRS. Various
experiments are conducted on benchmark datasets including MNIST, CIFAR, and
ImageNet-1K. Experimental results demonstrate the superiority of the proposed
LHS-BA over the state-of-the-art BA methods in terms of query efficiency. The
source codes are publicly available at https://github.com/GZHU-DVL/LHS-BA.
