Query-Efficient Adversarial Attack Based on Latin Hypercube Sampling. (arXiv:2207.02391v1 [cs.CV])

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 …

adversarial attack