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Adversarial Robustness and Fairness in Deep Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Adversarial Robustness and Fairness in Deep Learning./
Author:	Cherepanova, Valeriia.
Description:	1 online resource (135 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380580700

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

Adversarial Robustness and Fairness in Deep Learning. - 1 online resource (135 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380580700Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Adversarial robustnessIndex Terms--Genre/Form:

554714
Electronic books.

Adversarial Robustness and Fairness in Deep Learning.
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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 Deep learning
653 $a Face recognition
653 $a Fairness
653 $a Neural networks
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