  Randomized smoothing has emerged as a potent certifiable defense against
adversarial attacks by employing smoothing noises from specific distributions
to ensure the robustness of a smoothed classifier. However, the utilization of
Monte Carlo sampling in this process introduces a compute-intensive element,
which constrains the practicality of randomized smoothing on a larger scale. To
address this limitation, we propose a novel approach that replaces Monte Carlo
sampling with the training of a surrogate neural network. Through extensive
experimentation in various settings, we demonstrate the efficacy of our
approach in approximating the smoothed classifier with remarkable precision.
Furthermore, we demonstrate that our approach significantly accelerates the
robust radius certification process, providing nearly $600$X improvement in
computation time, overcoming the computational bottlenecks associated with
traditional randomized smoothing.
