A Novel Plug-and-Play Approach for Adversarially Robust Generalization

• URL: http://arxiv.org/abs/2208.09449v1
• Date: Fri, 19 Aug 2022 17:02:55 GMT
• Title: A Novel Plug-and-Play Approach for Adversarially Robust Generalization
• Authors: Deepak Maurya, Adarsh Barik, Jean Honorio
• Abstract summary: We propose a robust framework that employs adversarially robust training to safeguard the machine learning models against perturbed testing data. We achieve this by incorporating the worst-case additive adversarial error within a fixed budget for each sample during model estimation.
• Score: 26.29269757430314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose a robust framework that employs adversarially robust training to safeguard the machine learning models against perturbed testing data. We achieve this by incorporating the worst-case additive adversarial error within a fixed budget for each sample during model estimation. Our main focus is to provide a plug-and-play solution that can be incorporated in the existing machine learning algorithms with minimal changes. To that end, we derive the closed-form ready-to-use solution for several widely used loss functions with a variety of norm constraints on adversarial perturbation. Finally, we validate our approach by showing significant performance improvement on real-world datasets for supervised problems such as regression and classification, as well as for unsupervised problems such as matrix completion and learning graphical models, with very little computational overhead.

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