  This paper is a contribution to the reproducibility challenge in the field of
machine learning, specifically addressing the issue of certifying the
robustness of neural networks (NNs) against adversarial perturbations. The
proposed Double Sampling Randomized Smoothing (DSRS) framework overcomes the
limitations of existing methods by using an additional smoothing distribution
to improve the robustness certification. The paper provides a clear
manifestation of DSRS for a generalized family of Gaussian smoothing and a
computationally efficient method for implementation. The experiments on MNIST
and CIFAR-10 demonstrate the effectiveness of DSRS, consistently certifying
larger robust radii compared to other methods. Also various ablations studies
are conducted to further analyze the hyperparameters and effect of adversarial
training methods on the certified radius by the proposed framework.
