  Adversarial training is a computationally expensive task and hence searching
for neural network architectures with robustness as the criterion can be
challenging. As a step towards practical automation, this work explores the
efficacy of a simple post processing step in yielding robust deep learning
model. To achieve this, we adopt adversarial training as a post-processing step
for optimised network architectures obtained from a neural architecture search
algorithm. Specific policies are adopted for tuning the hyperparameters of the
different steps, resulting in a fully automated pipeline for generating
adversarially robust deep learning models. We evidence the usefulness of the
proposed pipeline with extensive experimentation across 11 image classification
and 9 text classification tasks.
