Assessing and Improving the Identification of Computer-Generated Portraits
Modern computer graphics are capable of generating highly photorealistic images. Although this can be considered a success for the computer graphics community, it has given rise to complex forensic and legal issues. A compelling example comes from the need to distinguish between computer-generated a...
Gespeichert in:
Veröffentlicht in: | ACM transactions on applied perception 2016-03, Vol.13 (2), p.1-12 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 12 |
---|---|
container_issue | 2 |
container_start_page | 1 |
container_title | ACM transactions on applied perception |
container_volume | 13 |
creator | Holmes, Olivia Banks, Martin S. Farid, Hany |
description | Modern computer graphics are capable of generating highly photorealistic images. Although this can be considered a success for the computer graphics community, it has given rise to complex forensic and legal issues. A compelling example comes from the need to distinguish between computer-generated and photographic images as it pertains to the legality and prosecution of child pornography in the United States. We performed psychophysical experiments to determine the accuracy with which observers are capable of distinguishing computer-generated from photographic images. We find that observers have considerable difficulty performing this task—more difficulty than we observed 5 years ago when computer-generated imagery was not as photorealistic. We also find that observers are more likely to report that an image is photographic rather than computer generated, and that resolution has surprisingly little effect on performance. Finally, we find that a small amount of training greatly improves accuracy. |
doi_str_mv | 10.1145/2871714 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1808048304</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1808048304</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-c9ed3ab548d1c980407bb21369c0e26a831ed8b6eada223b4c4f0585e863bd683</originalsourceid><addsrcrecordid>eNo1kE1LxDAYhIMouK7iX-hNL9V8dtPjUnStLOhBzyVN3mpk29S8qeC_t8uup5mBh2EYQq4ZvWNMqnuuV2zF5AlZMCVlLspCnf57pfQ5uUD8opTLUqkFeV4jAqIfPjIzuKzuxxh-9il9QlY7GJLvvDXJhyELXVaFfpwSxHwDA0STwGWvIaZofMJLctaZHcLVUZfk_fHhrXrKty-bulpvc8tLlnJbghOmVVI7ZktNJV21LWeiKC0FXhgtGDjdFmCc4Vy00sqOKq1AF6J1hRZLcnvonad-T4Cp6T1a2O3MAGHChmk6t2pB5YzeHFAbA2KErhmj7038bRht9m81x7fEH2dkW_4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1808048304</pqid></control><display><type>article</type><title>Assessing and Improving the Identification of Computer-Generated Portraits</title><source>ACM Digital Library Complete</source><creator>Holmes, Olivia ; Banks, Martin S. ; Farid, Hany</creator><creatorcontrib>Holmes, Olivia ; Banks, Martin S. ; Farid, Hany</creatorcontrib><description>Modern computer graphics are capable of generating highly photorealistic images. Although this can be considered a success for the computer graphics community, it has given rise to complex forensic and legal issues. A compelling example comes from the need to distinguish between computer-generated and photographic images as it pertains to the legality and prosecution of child pornography in the United States. We performed psychophysical experiments to determine the accuracy with which observers are capable of distinguishing computer-generated from photographic images. We find that observers have considerable difficulty performing this task—more difficulty than we observed 5 years ago when computer-generated imagery was not as photorealistic. We also find that observers are more likely to report that an image is photographic rather than computer generated, and that resolution has surprisingly little effect on performance. Finally, we find that a small amount of training greatly improves accuracy.</description><identifier>ISSN: 1544-3558</identifier><identifier>EISSN: 1544-3965</identifier><identifier>DOI: 10.1145/2871714</identifier><language>eng</language><subject>Accuracy ; Communities ; Computer graphics ; Observers ; Perception ; Photorealistic ; Prosecutions</subject><ispartof>ACM transactions on applied perception, 2016-03, Vol.13 (2), p.1-12</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-c9ed3ab548d1c980407bb21369c0e26a831ed8b6eada223b4c4f0585e863bd683</citedby><cites>FETCH-LOGICAL-c291t-c9ed3ab548d1c980407bb21369c0e26a831ed8b6eada223b4c4f0585e863bd683</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Holmes, Olivia</creatorcontrib><creatorcontrib>Banks, Martin S.</creatorcontrib><creatorcontrib>Farid, Hany</creatorcontrib><title>Assessing and Improving the Identification of Computer-Generated Portraits</title><title>ACM transactions on applied perception</title><description>Modern computer graphics are capable of generating highly photorealistic images. Although this can be considered a success for the computer graphics community, it has given rise to complex forensic and legal issues. A compelling example comes from the need to distinguish between computer-generated and photographic images as it pertains to the legality and prosecution of child pornography in the United States. We performed psychophysical experiments to determine the accuracy with which observers are capable of distinguishing computer-generated from photographic images. We find that observers have considerable difficulty performing this task—more difficulty than we observed 5 years ago when computer-generated imagery was not as photorealistic. We also find that observers are more likely to report that an image is photographic rather than computer generated, and that resolution has surprisingly little effect on performance. Finally, we find that a small amount of training greatly improves accuracy.</description><subject>Accuracy</subject><subject>Communities</subject><subject>Computer graphics</subject><subject>Observers</subject><subject>Perception</subject><subject>Photorealistic</subject><subject>Prosecutions</subject><issn>1544-3558</issn><issn>1544-3965</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNo1kE1LxDAYhIMouK7iX-hNL9V8dtPjUnStLOhBzyVN3mpk29S8qeC_t8uup5mBh2EYQq4ZvWNMqnuuV2zF5AlZMCVlLspCnf57pfQ5uUD8opTLUqkFeV4jAqIfPjIzuKzuxxh-9il9QlY7GJLvvDXJhyELXVaFfpwSxHwDA0STwGWvIaZofMJLctaZHcLVUZfk_fHhrXrKty-bulpvc8tLlnJbghOmVVI7ZktNJV21LWeiKC0FXhgtGDjdFmCc4Vy00sqOKq1AF6J1hRZLcnvonad-T4Cp6T1a2O3MAGHChmk6t2pB5YzeHFAbA2KErhmj7038bRht9m81x7fEH2dkW_4</recordid><startdate>20160301</startdate><enddate>20160301</enddate><creator>Holmes, Olivia</creator><creator>Banks, Martin S.</creator><creator>Farid, Hany</creator><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20160301</creationdate><title>Assessing and Improving the Identification of Computer-Generated Portraits</title><author>Holmes, Olivia ; Banks, Martin S. ; Farid, Hany</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-c9ed3ab548d1c980407bb21369c0e26a831ed8b6eada223b4c4f0585e863bd683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Accuracy</topic><topic>Communities</topic><topic>Computer graphics</topic><topic>Observers</topic><topic>Perception</topic><topic>Photorealistic</topic><topic>Prosecutions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Holmes, Olivia</creatorcontrib><creatorcontrib>Banks, Martin S.</creatorcontrib><creatorcontrib>Farid, Hany</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>ACM transactions on applied perception</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Holmes, Olivia</au><au>Banks, Martin S.</au><au>Farid, Hany</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing and Improving the Identification of Computer-Generated Portraits</atitle><jtitle>ACM transactions on applied perception</jtitle><date>2016-03-01</date><risdate>2016</risdate><volume>13</volume><issue>2</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>1544-3558</issn><eissn>1544-3965</eissn><abstract>Modern computer graphics are capable of generating highly photorealistic images. Although this can be considered a success for the computer graphics community, it has given rise to complex forensic and legal issues. A compelling example comes from the need to distinguish between computer-generated and photographic images as it pertains to the legality and prosecution of child pornography in the United States. We performed psychophysical experiments to determine the accuracy with which observers are capable of distinguishing computer-generated from photographic images. We find that observers have considerable difficulty performing this task—more difficulty than we observed 5 years ago when computer-generated imagery was not as photorealistic. We also find that observers are more likely to report that an image is photographic rather than computer generated, and that resolution has surprisingly little effect on performance. Finally, we find that a small amount of training greatly improves accuracy.</abstract><doi>10.1145/2871714</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1544-3558 |
ispartof | ACM transactions on applied perception, 2016-03, Vol.13 (2), p.1-12 |
issn | 1544-3558 1544-3965 |
language | eng |
recordid | cdi_proquest_miscellaneous_1808048304 |
source | ACM Digital Library Complete |
subjects | Accuracy Communities Computer graphics Observers Perception Photorealistic Prosecutions |
title | Assessing and Improving the Identification of Computer-Generated Portraits |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T13%3A17%3A33IST&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=Assessing%20and%20Improving%20the%20Identification%20of%20Computer-Generated%20Portraits&rft.jtitle=ACM%20transactions%20on%20applied%20perception&rft.au=Holmes,%20Olivia&rft.date=2016-03-01&rft.volume=13&rft.issue=2&rft.spage=1&rft.epage=12&rft.pages=1-12&rft.issn=1544-3558&rft.eissn=1544-3965&rft_id=info:doi/10.1145/2871714&rft_dat=%3Cproquest_cross%3E1808048304%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=1808048304&rft_id=info:pmid/&rfr_iscdi=true |