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Adversarial Robustness and Fairness in Deep Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adversarial Robustness and Fairness in Deep Learning.
作者:	Cherepanova, Valeriia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2023
面頁冊數:	135 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
附註:	Advisor: Goldstein, Tom.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Computer engineering.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30573585
ISBN:	9798380580700

Adversarial Robustness and Fairness in Deep Learning.
Cherepanova, Valeriia.

Adversarial Robustness and Fairness in Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 135 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2023.

This item must not be sold to any third party vendors.

While deep learning has led to remarkable advancements across various domains, the widespread adoption of neural network models has brought forth significant challenges such as vulnerability to adversarial attacks and model unfairness. These challenges have profound implications for privacy, security, and societal impact, requiring thorough investigation and development of effective mitigation strategies. In this work we address both these challenges. We study adversarial robustness of deep learning models and explore defense mechanisms against poisoning attacks. We also explore the sources of algorithmic bias and evaluate existing bias mitigation strategies in neural networks. Through this work, we aim to contribute to the understanding and enhancement of both adversarial robustness and fairness of deep learning systems.

ISBN: 9798380580700Subjects--Topical Terms:

212944
Computer engineering.
Subjects--Index Terms:

Adversarial robustness

Adversarial Robustness and Fairness in Deep Learning.
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500 $a Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
500 $a Advisor: Goldstein, Tom.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a While deep learning has led to remarkable advancements across various domains, the widespread adoption of neural network models has brought forth significant challenges such as vulnerability to adversarial attacks and model unfairness. These challenges have profound implications for privacy, security, and societal impact, requiring thorough investigation and development of effective mitigation strategies. In this work we address both these challenges. We study adversarial robustness of deep learning models and explore defense mechanisms against poisoning attacks. We also explore the sources of algorithmic bias and evaluate existing bias mitigation strategies in neural networks. Through this work, we aim to contribute to the understanding and enhancement of both adversarial robustness and fairness of deep learning systems.
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653 $a Fairness
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