  Certified defense methods against adversarial perturbations have been
recently investigated in the black-box setting with a zeroth-order (ZO)
perspective. However, these methods suffer from high model variance with low
performance on high-dimensional datasets due to the ineffective design of the
denoiser and are limited in their utilization of ZO techniques. To this end, we
propose a certified ZO preprocessing technique for removing adversarial
perturbations from the attacked image in the black-box setting using only model
queries. We propose a robust UNet denoiser (RDUNet) that ensures the robustness
of black-box models trained on high-dimensional datasets. We propose a novel
black-box denoised smoothing (DS) defense mechanism, ZO-RUDS, by prepending our
RDUNet to the black-box model, ensuring black-box defense. We further propose
ZO-AE-RUDS in which RDUNet followed by autoencoder (AE) is prepended to the
black-box model. We perform extensive experiments on four classification
datasets, CIFAR-10, CIFAR-10, Tiny Imagenet, STL-10, and the MNIST dataset for
image reconstruction tasks. Our proposed defense methods ZO-RUDS and ZO-AE-RUDS
beat SOTA with a huge margin of $35\%$ and $9\%$, for low dimensional
(CIFAR-10) and with a margin of $20.61\%$ and $23.51\%$ for high-dimensional
(STL-10) datasets, respectively.
