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Assessing Vulnerability in Variational Autoencoder GANs : = Insights From White Box and Black Box Attack Scenarios.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Assessing Vulnerability in Variational Autoencoder GANs :/
Reminder of title:	Insights From White Box and Black Box Attack Scenarios.
Author:	Tangirala, Srivatsa.
Description:	1 online resource (43 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798383057704

Assessing Vulnerability in Variational Autoencoder GANs : = Insights From White Box and Black Box Attack Scenarios.
Tangirala, Srivatsa.

Assessing Vulnerability in Variational Autoencoder GANs :Insights From White Box and Black Box Attack Scenarios. - 1 online resource (43 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2024.

Includes bibliographical references

In this study, we explore the vulnerability of Variational Autoencoder Generative Adversarial Networks (VAEGANs) to membership inference attacks, focusing on the potential privacy risks associated with Generative AI. By employing both black-box and white-box attack methodologies, we aim to quantify the extent to which sensitive information can be discerned or reconstructed by an adversary. Our investigation reveals significant vulnerabilities within the VAEGAN framework, highlighting the pressing need for enhanced privacy protections in generative models. Through a series of experiments across diverse datasets, we assess the impact of these attacks on the integrity of the models, shedding light on the balance between data generation capabilities and privacy preservation. Our findings underscore the importance of developing robust mechanisms to safeguard against such privacy breaches, contributing to the broader discussion on securing Generative AI against emerging threats.

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

Mode of access: World Wide Web

ISBN: 9798383057704Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Privacy preservationIndex Terms--Genre/Form:

554714
Electronic books.

Assessing Vulnerability in Variational Autoencoder GANs : = Insights From White Box and Black Box Attack Scenarios.
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504 $a Includes bibliographical references
520 $a In this study, we explore the vulnerability of Variational Autoencoder Generative Adversarial Networks (VAEGANs) to membership inference attacks, focusing on the potential privacy risks associated with Generative AI. By employing both black-box and white-box attack methodologies, we aim to quantify the extent to which sensitive information can be discerned or reconstructed by an adversary. Our investigation reveals significant vulnerabilities within the VAEGAN framework, highlighting the pressing need for enhanced privacy protections in generative models. Through a series of experiments across diverse datasets, we assess the impact of these attacks on the integrity of the models, shedding light on the balance between data generation capabilities and privacy preservation. Our findings underscore the importance of developing robust mechanisms to safeguard against such privacy breaches, contributing to the broader discussion on securing Generative AI against emerging threats.
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