{"id":"https://openalex.org/W4379468553","doi":"https://doi.org/10.1109/access.2023.3282927","title":"Model Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors","display_name":"Model Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4379468553","doi":"https://doi.org/10.1109/access.2023.3282927"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3282927","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3282927","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10143645.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10143645.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020327450","display_name":"Jacob Piland","orcid":"https://orcid.org/0009-0000-6140-7734"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jacob C. Piland","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":"https://orcid.org/0009-0000-6140-7734","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067121774","display_name":"Adam Czajka","orcid":"https://orcid.org/0000-0003-2379-2533"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adam Czajka","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":"https://orcid.org/0000-0003-2379-2533","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103104238","display_name":"Christopher Sweet","orcid":"https://orcid.org/0000-0001-8214-7177"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher Sweet","raw_affiliation_strings":["Center for Research Computing, University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Research Computing, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.8982,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75918904,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"11","issue":null,"first_page":"63430","last_page":"63441"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7000426054000854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6435452699661255},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6087909936904907},{"id":"https://openalex.org/keywords/salience","display_name":"Salience (neuroscience)","score":0.4991419315338135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4913579523563385},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4586408734321594},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4110642373561859},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.39927899837493896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37888723611831665},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.19771328568458557}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7000426054000854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6435452699661255},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6087909936904907},{"id":"https://openalex.org/C108154423","wikidata":"https://www.wikidata.org/wiki/Q1469792","display_name":"Salience (neuroscience)","level":2,"score":0.4991419315338135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4913579523563385},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4586408734321594},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4110642373561859},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.39927899837493896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37888723611831665},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.19771328568458557},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3282927","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3282927","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10143645.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a1db8ceddc534529a4871f478e5f5dbb","is_oa":true,"landing_page_url":"https://doaj.org/article/a1db8ceddc534529a4871f478e5f5dbb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 63430-63441 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3282927","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3282927","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10143645.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6742131198","display_name":null,"funder_award_id":"W52P1J2093009","funder_id":"https://openalex.org/F4320306078","funder_display_name":"U.S. Department of Defense"}],"funders":[{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379468553.pdf","grobid_xml":"https://content.openalex.org/works/W4379468553.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1502364872","https://openalex.org/W2066117401","https://openalex.org/W2068734928","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2531409750","https://openalex.org/W2811414481","https://openalex.org/W2943520232","https://openalex.org/W2950352474","https://openalex.org/W2952716587","https://openalex.org/W2962770929","https://openalex.org/W2963080198","https://openalex.org/W2963446712","https://openalex.org/W2963544815","https://openalex.org/W2964277347","https://openalex.org/W2965128575","https://openalex.org/W2990564716","https://openalex.org/W3012033723","https://openalex.org/W3034577585","https://openalex.org/W3034600949","https://openalex.org/W3034720584","https://openalex.org/W3035574324","https://openalex.org/W3041498386","https://openalex.org/W3083683318","https://openalex.org/W3094728142","https://openalex.org/W3120110650","https://openalex.org/W3158826226","https://openalex.org/W3162881418","https://openalex.org/W3174205643","https://openalex.org/W3174807077","https://openalex.org/W3196620563","https://openalex.org/W4225976427","https://openalex.org/W4226032084","https://openalex.org/W4294643831","https://openalex.org/W4320013936","https://openalex.org/W6745560452","https://openalex.org/W6755312952","https://openalex.org/W6771275388","https://openalex.org/W6775642233","https://openalex.org/W6779093361","https://openalex.org/W6794339395","https://openalex.org/W6797179183","https://openalex.org/W6804691536"],"related_works":["https://openalex.org/W2117158897","https://openalex.org/W2273780664","https://openalex.org/W575883583","https://openalex.org/W3041490575","https://openalex.org/W2970690932","https://openalex.org/W4321135767","https://openalex.org/W3163690399","https://openalex.org/W4385327802","https://openalex.org/W2086372684","https://openalex.org/W2994927414"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"models":[2,46,144,244],"generalize":[3,246],"better":[4,147,247],"to":[5,13,41,116,143,175,178,222,245,269],"unknown":[6,160],"data":[7],"samples":[8,156],"after":[9],"being":[10],"guided":[11],"\u201cwhere":[12],"look\u201d":[14],"by":[15,56,137,159],"incorporating":[16],"human":[17,49,84,223,261],"perception":[18],"into":[19],"training":[20,47],"strategies.":[21],"We":[22,120,192],"made":[23],"an":[24,149,239,249],"observation":[25],"that":[26,35,129,145,195,232],"the":[27,30,62,73,78,101,105,109,131,138,154,163,168,189,201,213,219,235,243,277],"entropy":[28,43,63,136],"of":[29,64,77,108,111,125,188,212,218,234,258],"model\u2019s":[31,65,86,102,132,190,202,214,220,236,264],"salience":[32,42,265],"trained":[33],"in":[34,70,148,248,273],"way":[36],"is":[37,59,198,231,238],"lower":[38],"when":[39,200],"compared":[40,177],"computed":[44],"for":[45,83,183],"without":[48],"perceptual":[50],"intelligence.":[51],"The":[52,225],"research":[53],"problem":[54,124],"addressed":[55],"this":[57,89,229],"paper":[58,90],"whether":[60],"lowering":[61,135],"class":[66],"activation":[67],"map":[68],"helps":[69],"further":[71],"increasing":[72],"performance,":[74,210],"on":[75],"top":[76],"performance":[79,197,272],"increase":[80],"we":[81,91],"observe":[82],"saliency-based":[85],"training.":[87],"In":[88],"propose":[92],"and":[93,216,263],"evaluate":[94],"four":[95],"new":[96],"entropy-based":[97,266],"loss":[98,140,203,267],"functions":[99],"controlling":[100],"focus,":[103,133,215],"covering":[104],"full":[106],"range":[107],"level":[110],"such":[112],"control,":[113],"from":[114,173,228],"none":[115],"its":[117],"\u201caggresive\u201d":[118],"minimization.":[119],"show,":[121],"using":[122],"a":[123,184],"synthetic":[126,278],"face":[127,279],"detection,":[128],"improving":[130],"through":[134],"proposed":[139],"components,":[141],"leads":[142],"perform":[146],"open-set":[150],"scenario":[151],"(in":[152],"which":[153],"test":[155],"are":[157],"synthesized":[158],"generative":[161],"models):":[162],"obtained":[164,199],"average":[165],"Area":[166],"Under":[167],"ROC":[169],"curve":[170],"(AUROC)":[171],"ranges":[172],"0.72":[174],"0.78,":[176],"AUROC":[179],"=":[180],"0.64":[181],"observed":[182],"state-of-the-art":[185],"human-salience-only-based":[186],"control":[187],"focus.":[191],"also":[193],"show":[194],"optimal":[196,271],"function":[204],"blends":[205],"three":[206],"aspects:":[207],"regular":[208],"classification":[209],"low-entropy":[211],"closeness":[217],"focus":[221,237],"saliency.":[224],"major":[226],"conclusion":[227],"work":[230,254],"maximization":[233],"important":[240],"regularizer":[241],"allowing":[242],"open":[250],"set":[251],"scenario.":[252],"Future":[253],"directions":[255],"include":[256],"methods":[257],"blending":[259],"classification-,":[260],"salience-,":[262],"components":[268],"achieve":[270],"other":[274],"domains":[275],"than":[276],"detection.":[280]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}