Humans Versus Machines: A Deepfake Detection Faceoff
ABSTRACT Machine learning (ML) models for deepfake detection are important for countering the threat of such videos. However, human detection is also critical because automated approaches may not always be available to people online. This study compares ML models versus humans for deepfake detection...
Gespeichert in:
Veröffentlicht in: | Proceedings of the ASIST Annual Meeting 2024-10, Vol.61 (1), p.917-919 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 919 |
---|---|
container_issue | 1 |
container_start_page | 917 |
container_title | Proceedings of the ASIST Annual Meeting |
container_volume | 61 |
creator | Goh, Dion Hoe‐Lian Pan, Jonathan Lee, Chei Sian |
description | ABSTRACT
Machine learning (ML) models for deepfake detection are important for countering the threat of such videos. However, human detection is also critical because automated approaches may not always be available to people online. This study compares ML models versus humans for deepfake detection. Results surprisingly showed that humans performed better. Implications of our work are discussed. |
doi_str_mv | 10.1002/pra2.1139 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3116545600</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3116545600</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1029-128ee0c58affb1292e7afd80997d395bec4a10bee63634de08757389b4b2d1ae3</originalsourceid><addsrcrecordid>eNp1kD1PwzAURS0EElXpwD-IxMSQ9j07X2arCqVIRSAErJbjPIuUNgl2o6r_noQwsDDdO5z3rnQYu0SYIgCfNU7zKaKQJ2zERSpCyQWe_unnbOL9BgAwS0QqxYhFq3anKx-8k_OtDx61-Sgr8jfBPLglaqz-pK7syezLugqW2lBt7QU7s3rrafKbY_a2vHtdrML10_3DYr4ODQKXIfKMCEycaWtz5JJTqm2RgZRpIWSck4k0Qk6UiEREBUGWxqnIZB7lvEBNYsyuhr-Nq79a8nu1qVtXdZNKICZxFCcAHXU9UMbV3juyqnHlTrujQlC9F9V7Ub2Xjp0N7KHc0vF_UD2_zPnPxTfYfWHq</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3116545600</pqid></control><display><type>article</type><title>Humans Versus Machines: A Deepfake Detection Faceoff</title><source>Alma/SFX Local Collection</source><creator>Goh, Dion Hoe‐Lian ; Pan, Jonathan ; Lee, Chei Sian</creator><creatorcontrib>Goh, Dion Hoe‐Lian ; Pan, Jonathan ; Lee, Chei Sian</creatorcontrib><description>ABSTRACT
Machine learning (ML) models for deepfake detection are important for countering the threat of such videos. However, human detection is also critical because automated approaches may not always be available to people online. This study compares ML models versus humans for deepfake detection. Results surprisingly showed that humans performed better. Implications of our work are discussed.</description><identifier>ISSN: 2373-9231</identifier><identifier>EISSN: 2373-9231</identifier><identifier>EISSN: 1550-8390</identifier><identifier>DOI: 10.1002/pra2.1139</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Accuracy ; Deception ; Deepfake detection ; Human detection ; Machine learning ; Machine learning models</subject><ispartof>Proceedings of the ASIST Annual Meeting, 2024-10, Vol.61 (1), p.917-919</ispartof><rights>87 Annual Meeting of the Association for Information Science & Technology | Oct. 25 – 29, 2024 | Calgary, AB, Canada</rights><rights>2024 ASIS&T</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1029-128ee0c58affb1292e7afd80997d395bec4a10bee63634de08757389b4b2d1ae3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Goh, Dion Hoe‐Lian</creatorcontrib><creatorcontrib>Pan, Jonathan</creatorcontrib><creatorcontrib>Lee, Chei Sian</creatorcontrib><title>Humans Versus Machines: A Deepfake Detection Faceoff</title><title>Proceedings of the ASIST Annual Meeting</title><description>ABSTRACT
Machine learning (ML) models for deepfake detection are important for countering the threat of such videos. However, human detection is also critical because automated approaches may not always be available to people online. This study compares ML models versus humans for deepfake detection. Results surprisingly showed that humans performed better. Implications of our work are discussed.</description><subject>Accuracy</subject><subject>Deception</subject><subject>Deepfake detection</subject><subject>Human detection</subject><subject>Machine learning</subject><subject>Machine learning models</subject><issn>2373-9231</issn><issn>2373-9231</issn><issn>1550-8390</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp1kD1PwzAURS0EElXpwD-IxMSQ9j07X2arCqVIRSAErJbjPIuUNgl2o6r_noQwsDDdO5z3rnQYu0SYIgCfNU7zKaKQJ2zERSpCyQWe_unnbOL9BgAwS0QqxYhFq3anKx-8k_OtDx61-Sgr8jfBPLglaqz-pK7syezLugqW2lBt7QU7s3rrafKbY_a2vHtdrML10_3DYr4ODQKXIfKMCEycaWtz5JJTqm2RgZRpIWSck4k0Qk6UiEREBUGWxqnIZB7lvEBNYsyuhr-Nq79a8nu1qVtXdZNKICZxFCcAHXU9UMbV3juyqnHlTrujQlC9F9V7Ub2Xjp0N7KHc0vF_UD2_zPnPxTfYfWHq</recordid><startdate>202410</startdate><enddate>202410</enddate><creator>Goh, Dion Hoe‐Lian</creator><creator>Pan, Jonathan</creator><creator>Lee, Chei Sian</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope></search><sort><creationdate>202410</creationdate><title>Humans Versus Machines: A Deepfake Detection Faceoff</title><author>Goh, Dion Hoe‐Lian ; Pan, Jonathan ; Lee, Chei Sian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1029-128ee0c58affb1292e7afd80997d395bec4a10bee63634de08757389b4b2d1ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Deception</topic><topic>Deepfake detection</topic><topic>Human detection</topic><topic>Machine learning</topic><topic>Machine learning models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goh, Dion Hoe‐Lian</creatorcontrib><creatorcontrib>Pan, Jonathan</creatorcontrib><creatorcontrib>Lee, Chei Sian</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Proceedings of the ASIST Annual Meeting</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goh, Dion Hoe‐Lian</au><au>Pan, Jonathan</au><au>Lee, Chei Sian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Humans Versus Machines: A Deepfake Detection Faceoff</atitle><jtitle>Proceedings of the ASIST Annual Meeting</jtitle><date>2024-10</date><risdate>2024</risdate><volume>61</volume><issue>1</issue><spage>917</spage><epage>919</epage><pages>917-919</pages><issn>2373-9231</issn><eissn>2373-9231</eissn><eissn>1550-8390</eissn><abstract>ABSTRACT
Machine learning (ML) models for deepfake detection are important for countering the threat of such videos. However, human detection is also critical because automated approaches may not always be available to people online. This study compares ML models versus humans for deepfake detection. Results surprisingly showed that humans performed better. Implications of our work are discussed.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/pra2.1139</doi><tpages>3</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2373-9231 |
ispartof | Proceedings of the ASIST Annual Meeting, 2024-10, Vol.61 (1), p.917-919 |
issn | 2373-9231 2373-9231 1550-8390 |
language | eng |
recordid | cdi_proquest_journals_3116545600 |
source | Alma/SFX Local Collection |
subjects | Accuracy Deception Deepfake detection Human detection Machine learning Machine learning models |
title | Humans Versus Machines: A Deepfake Detection Faceoff |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T22%3A07%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Humans%20Versus%20Machines:%20A%20Deepfake%20Detection%20Faceoff&rft.jtitle=Proceedings%20of%20the%20ASIST%20Annual%20Meeting&rft.au=Goh,%20Dion%20Hoe%E2%80%90Lian&rft.date=2024-10&rft.volume=61&rft.issue=1&rft.spage=917&rft.epage=919&rft.pages=917-919&rft.issn=2373-9231&rft.eissn=2373-9231&rft_id=info:doi/10.1002/pra2.1139&rft_dat=%3Cproquest_cross%3E3116545600%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3116545600&rft_id=info:pmid/&rfr_iscdi=true |