{"id":"https://openalex.org/W6906802355","doi":"https://doi.org/10.18420/biosig2025_17","title":"Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection","display_name":"Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W6906802355","doi":"https://doi.org/10.18420/biosig2025_17"},"language":"en","primary_location":{"id":"doi:10.18420/biosig2025_17","is_oa":true,"landing_page_url":"https://doi.org/10.18420/biosig2025_17","pdf_url":null,"source":{"id":"https://openalex.org/S7407052918","display_name":"Gesellschaft f\u00fcr Informatik (GI)","issn_l":null,"issn":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.18420/biosig2025_17","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Furuhashi, Yoshihiko","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Furuhashi, Yoshihiko","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yamagishi, Junichi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yamagishi, Junichi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Xin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Nguyen, Huy Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Huy Hong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Echizen, Isao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Echizen, Isao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3488262,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8080000281333923,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8080000281333923,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.051600001752376556,"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/T11019","display_name":"Image Enhancement Techniques","score":0.02370000071823597,"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/training-set","display_name":"Training set","score":0.673799991607666},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5570999979972839},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5146999955177307},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.476500004529953},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.47209998965263367},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.3677999973297119}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825000286102295},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.673799991607666},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5570999979972839},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5146999955177307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.491100013256073},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.476500004529953},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.47209998965263367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4453999996185303},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3677999973297119},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C2779280203","wikidata":"https://www.wikidata.org/wiki/Q17121211","display_name":"Small data","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18420/biosig2025_17","is_oa":true,"landing_page_url":"https://doi.org/10.18420/biosig2025_17","pdf_url":null,"source":{"id":"https://openalex.org/S7407052918","display_name":"Gesellschaft f\u00fcr Informatik (GI)","issn_l":null,"issn":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.18420/biosig2025_17","is_oa":true,"landing_page_url":"https://doi.org/10.18420/biosig2025_17","pdf_url":null,"source":{"id":"https://openalex.org/S7407052918","display_name":"Gesellschaft f\u00fcr Informatik (GI)","issn_l":null,"issn":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,22],"deepfake":[1,19,46,52,80,91,102],"detection,":[2],"it":[3],"is":[4],"essential":[5],"to":[6,29,118],"maintain":[7],"high":[8],"performance":[9],"by":[10,78],"adjusting":[11],"the":[12,15,34,42,86,90,101,119,127,139,144],"parameters":[13],"of":[14,37,45,75,89,111,133,138,141],"detector":[16],"as":[17,94],"new":[18,63,79],"methods":[20,81],"emerge.":[21],"this":[23],"paper,":[24],"we":[25],"propose":[26],"a":[27,67,72,95,108],"method":[28,60],"automatically":[30,61,114],"and":[31,82,116,124],"actively":[32],"select":[33],"small":[35,109],"amount":[36,110,140],"additional":[38,112],"data":[39,65,113,142],"required":[40],"for":[41],"continuous":[43],"training":[44,64,121],"detection":[47,53,92,103,128],"models":[48,54],"in":[49,143],"situations":[50],"where":[51],"are":[55],"regularly":[56],"updated.":[57],"The":[58],"proposed":[59],"selects":[62],"from":[66],"redundant":[68],"pool":[69,145],"set":[70],"containing":[71],"large":[73],"number":[74],"images":[76],"generated":[77],"real":[83],"images,":[84],"using":[85],"confidence":[87],"score":[88],"model":[93],"metric.":[96],"Experimental":[97],"results":[98],"show":[99],"that":[100],"model,":[104],"continuously":[105],"trained":[106],"with":[107,135],"selected":[115],"added":[117],"original":[120],"set,":[122],"significantly":[123],"efficiently":[125],"improved":[126],"performance,":[129],"achieving":[130],"an":[131],"EER":[132],"2.5%":[134],"only":[136],"15%":[137],"set.":[146]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
