Related papers: Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications

Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications

URL: http://arxiv.org/abs/2108.10451v1
Date: Tue, 24 Aug 2021 00:08:33 GMT
Title: Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications
Authors: Wenjie Ruan and Xinping Yi and Xiaowei Huang
Abstract summary: This tutorial aims to introduce the fundamentals of adversarial robustness of deep learning. We will highlight state-of-the-art techniques in adversarial attacks and robustness verification of deep neural networks (DNNs) We will also introduce some effective countermeasures to improve the robustness of deep learning models.
Score: 27.033174829788404
License: http://creativecommons.org/licenses/by/4.0/
Abstract: This tutorial aims to introduce the fundamentals of adversarial robustness of deep learning, presenting a well-structured review of up-to-date techniques to assess the vulnerability of various types of deep learning models to adversarial examples. This tutorial will particularly highlight state-of-the-art techniques in adversarial attacks and robustness verification of deep neural networks (DNNs). We will also introduce some effective countermeasures to improve the robustness of deep learning models, with a particular focus on adversarial training. We aim to provide a comprehensive overall picture about this emerging direction and enable the community to be aware of the urgency and importance of designing robust deep learning models in safety-critical data analytical applications, ultimately enabling the end-users to trust deep learning classifiers. We will also summarize potential research directions concerning the adversarial robustness of deep learning, and its potential benefits to enable accountable and trustworthy deep learning-based data analytical systems and applications.

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