Deepfakes Recognition with Physiological Signals

A Master of Science thesis in Electrical Engineering by Muhammad Riyyan Khan entitled, “Deepfakes Recognition with Physiological Signals”, submitted in April 2024. Thesis advisor is Dr. Usman Tariq and thesis co-advisors are Dr. Hasan Al-Nashash and Dr. Abhinav Dhall. Soft copy is available (Thesis,...

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Main Author: Khan, Muhammad Riyyan (author)
Format: doctoralThesis
Published: 2024
Subjects:
Online Access:https://hdl.handle.net/11073/25623
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author Khan, Muhammad Riyyan
author_facet Khan, Muhammad Riyyan
author_role author
dc.contributor.none.fl_str_mv Tariq, Usman
Al-Nashash, Hasan
Dhall, Abhinav
dc.creator.none.fl_str_mv Khan, Muhammad Riyyan
dc.date.none.fl_str_mv 2024-09-25T07:30:54Z
2024-09-25T07:30:54Z
2024-04
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv 35.232-2024.31
https://hdl.handle.net/11073/25623
dc.language.none.fl_str_mv en_US
dc.subject.none.fl_str_mv Deepfake videos
Deepfake detection methods
Physiological Signals
dc.title.none.fl_str_mv Deepfakes Recognition with Physiological Signals
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/doctoralThesis
description A Master of Science thesis in Electrical Engineering by Muhammad Riyyan Khan entitled, “Deepfakes Recognition with Physiological Signals”, submitted in April 2024. Thesis advisor is Dr. Usman Tariq and thesis co-advisors are Dr. Hasan Al-Nashash and Dr. Abhinav Dhall. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
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identifier_str_mv 35.232-2024.31
language_invalid_str_mv en_US
network_acronym_str aus
network_name_str aus
oai_identifier_str oai:repository.aus.edu:11073/25623
publishDate 2024
repository.mail.fl_str_mv
repository.name.fl_str_mv
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spelling Deepfakes Recognition with Physiological SignalsKhan, Muhammad RiyyanDeepfake videosDeepfake detection methodsPhysiological SignalsA Master of Science thesis in Electrical Engineering by Muhammad Riyyan Khan entitled, “Deepfakes Recognition with Physiological Signals”, submitted in April 2024. Thesis advisor is Dr. Usman Tariq and thesis co-advisors are Dr. Hasan Al-Nashash and Dr. Abhinav Dhall. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE)Tariq, UsmanAl-Nashash, HasanDhall, Abhinav2024-09-25T07:30:54Z2024-09-25T07:30:54Z2024-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2024.31https://hdl.handle.net/11073/25623en_USoai:repository.aus.edu:11073/256232025-06-26T12:22:02Z
spellingShingle Deepfakes Recognition with Physiological Signals
Khan, Muhammad Riyyan
Deepfake videos
Deepfake detection methods
Physiological Signals
status_str publishedVersion
title Deepfakes Recognition with Physiological Signals
title_full Deepfakes Recognition with Physiological Signals
title_fullStr Deepfakes Recognition with Physiological Signals
title_full_unstemmed Deepfakes Recognition with Physiological Signals
title_short Deepfakes Recognition with Physiological Signals
title_sort Deepfakes Recognition with Physiological Signals
topic Deepfake videos
Deepfake detection methods
Physiological Signals
url https://hdl.handle.net/11073/25623