Review of adversarial attacks and defenses on edge machine learning

This project aims to analyse the various Adversarial Threats to Machine Learning on the Edge and how they can be mitigated by Trusted Execution Environment (TEE). This report will analyse the effectiveness of the TEE in mitigating these threats and where it can be supplemented by other Adversarial D...

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Bibliographic Details
Main Author:	Chua, Jim Sean
Other Authors:	Anupam Chattopadhyay
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2024
Subjects:	Computer and Information Science
Online Access:	https://hdl.handle.net/10356/175152
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-175152
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spelling	sg-ntu-dr.10356-1751522024-04-26T15:41:06Z Review of adversarial attacks and defenses on edge machine learning Chua, Jim Sean Anupam Chattopadhyay School of Computer Science and Engineering anupam@ntu.edu.sg Computer and Information Science This project aims to analyse the various Adversarial Threats to Machine Learning on the Edge and how they can be mitigated by Trusted Execution Environment (TEE). This report will analyse the effectiveness of the TEE in mitigating these threats and where it can be supplemented by other Adversarial Defenses in the Edge setting. Bachelor's degree 2024-04-22T07:05:07Z 2024-04-22T07:05:07Z 2024 Final Year Project (FYP) Chua, J. S. (2024). Review of adversarial attacks and defenses on edge machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175152 https://hdl.handle.net/10356/175152 en application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Computer and Information Science
spellingShingle	Computer and Information Science Chua, Jim Sean Review of adversarial attacks and defenses on edge machine learning
description	This project aims to analyse the various Adversarial Threats to Machine Learning on the Edge and how they can be mitigated by Trusted Execution Environment (TEE). This report will analyse the effectiveness of the TEE in mitigating these threats and where it can be supplemented by other Adversarial Defenses in the Edge setting.
author2	Anupam Chattopadhyay
author_facet	Anupam Chattopadhyay Chua, Jim Sean
format	Final Year Project
author	Chua, Jim Sean
author_sort	Chua, Jim Sean
title	Review of adversarial attacks and defenses on edge machine learning
title_short	Review of adversarial attacks and defenses on edge machine learning
title_full	Review of adversarial attacks and defenses on edge machine learning
title_fullStr	Review of adversarial attacks and defenses on edge machine learning
title_full_unstemmed	Review of adversarial attacks and defenses on edge machine learning
title_sort	review of adversarial attacks and defenses on edge machine learning
publisher	Nanyang Technological University
publishDate	2024
url	https://hdl.handle.net/10356/175152
_version_	1800916340287995904

Review of adversarial attacks and defenses on edge machine learning

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