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arXiv · 2109.02532

Automated Robustness with Adversarial Training as a Post-Processing Step

Ambrish Rawat·Mathieu Sinn·Beat Buesser
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Abstract

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.

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Ambrish Rawat, Mathieu Sinn, Beat Buesser. 2021-09-06. Automated Robustness with Adversarial Training as a Post-Processing Step. https://arxiv.org/abs/2109.02532

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