Publication
AAAI 2022
Tutorial

Adversarial Machine Learning for Good

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Abstract

Unlike conventional tutorials on adversarial machine learning (AdvML) that focus on adversarial attacks, defenses, or verification methods, this tutorial aims to provide a fresh overview of how the same technique can be used in totally different manners to benefit mainstream machine learning tasks and to facilitate sustainable growth in this research field. This tutorial will start by reviewing the recent advances in AdvML and then delve into novel innovations to other domains (beyond adversarial robustness) inspired from AdvML. In particular, we will cover several noteworthy innovations proposed in recent years and relate their success to AdvML, including (i) generation of contrastive explanations and counterfactual examples; (ii) model reprogramming for data-efficient transfer learning; (iii) model watermarking and fingerprinting for AI governance and ownership regulation; (iv) data cloaking for enhanced privacy Second; and (v) data augmentation for improving model generalization. Finally, this tutorial will discuss the sustainability of this research field towards continuous and organic growth, in terms of research norms and ethics, current trends, open challenges, and future directions.

Date

21 Feb 2022

Publication

AAAI 2022

Authors

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