{"id":"https://openalex.org/W4415193662","doi":"https://doi.org/10.1109/wacv61042.2026.00082","title":"Deepfake Detection that Generalizes Across Benchmarks","display_name":"Deepfake Detection that Generalizes Across Benchmarks","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W4415193662","doi":"https://doi.org/10.1109/wacv61042.2026.00082"},"language":"en","primary_location":{"id":"doi:10.1109/wacv61042.2026.00082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.06248","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033869972","display_name":"Andrii Yermakov","orcid":"https://orcid.org/0000-0002-2173-5095"},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Andrii Yermakov","raw_affiliation_strings":["Czech Technical University,Prague"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Czech Technical University,Prague","institution_ids":["https://openalex.org/I44504214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018876350","display_name":"Jan \u010cech","orcid":"https://orcid.org/0000-0002-2181-5917"},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Jan Cech","raw_affiliation_strings":["Czech Technical University,Prague"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Czech Technical University,Prague","institution_ids":["https://openalex.org/I44504214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007656938","display_name":"Ji\u0159\u0131\u0301 Matas","orcid":"https://orcid.org/0000-0003-0863-4844"},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Jiri Matas","raw_affiliation_strings":["Czech Technical University,Prague"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Czech Technical University,Prague","institution_ids":["https://openalex.org/I44504214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003887059","display_name":"Mario Fritz","orcid":"https://orcid.org/0000-0001-8949-9896"},"institutions":[{"id":"https://openalex.org/I4210128801","display_name":"Helmholtz Center for Information Security","ror":"https://ror.org/02njgxr09","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I4210128801"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mario Fritz","raw_affiliation_strings":["CISPA Helmholtz Center for Information Security"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CISPA Helmholtz Center for Information Security","institution_ids":["https://openalex.org/I4210128801"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"773","last_page":"783"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8531000018119812,"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":0.8531000018119812,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7782999873161316,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7239000201225281,"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/generalization","display_name":"Generalization","score":0.76419997215271},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6985999941825867},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5030999779701233},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.47450000047683716},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.46549999713897705},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.4309999942779541},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.39660000801086426},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3865000009536743}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.76419997215271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7240999937057495},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6985999941825867},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.597100019454956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.564300000667572},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47450000047683716},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.46549999713897705},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.39660000801086426},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.31200000643730164},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2888999879360199},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2847000062465668},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":8,"locations":[{"id":"doi:10.1109/wacv61042.2026.00082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.06248","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.06248","pdf_url":"https://arxiv.org/pdf/2508.06248","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:arXiv.org:2508.06248","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2508.06248","pdf_url":"https://arxiv.org/pdf/2508.06248","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2508.06248","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.06248","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.60882/cispa.32781279","is_oa":true,"landing_page_url":"https://doi.org/10.60882/cispa.32781279","pdf_url":null,"source":{"id":"https://openalex.org/S7407050916","display_name":"CISPA Helmholtz Center","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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.60882/cispa.32781279.v1","is_oa":true,"landing_page_url":"https://doi.org/10.60882/cispa.32781279.v1","pdf_url":null,"source":{"id":"https://openalex.org/S7407050916","display_name":"CISPA Helmholtz Center","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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.60882/cispa.32781444","is_oa":true,"landing_page_url":"https://doi.org/10.60882/cispa.32781444","pdf_url":null,"source":{"id":"https://openalex.org/S7407050916","display_name":"CISPA Helmholtz Center","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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"ConferenceProceeding"},{"id":"doi:10.60882/cispa.32781444.v1","is_oa":true,"landing_page_url":"https://doi.org/10.60882/cispa.32781444.v1","pdf_url":null,"source":{"id":"https://openalex.org/S7407050916","display_name":"CISPA Helmholtz Center","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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"ConferenceProceeding"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.06248","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.06248","pdf_url":"https://arxiv.org/pdf/2508.06248","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"The":[0,46,91,186],"generalization":[1,31,62,157,171],"of":[2,38,40,57],"deepfake":[3],"detectors":[4],"to":[5,89,179],"unseen":[6],"manipulation":[7],"techniques":[8],"remains":[9],"a":[10,35,65,162,180],"challenge":[11],"for":[12,112,128],"practical":[13],"deployment.":[14],"Although":[15],"many":[16],"approaches":[17,101],"adapt":[18],"foundation":[19],"models":[20,149],"by":[21,63,174],"introducing":[22],"significant":[23],"architectural":[24],"complexity,":[25],"this":[26],"work":[27,160],"demonstrates":[28],"that":[29,169],"robust":[30],"is":[32,126,172,188],"achievable":[33],"through":[34],"parameter-efficient":[36],"adaptation":[37],"one":[39],"the":[41,52,58,113,122],"foundational":[42,182],"pre-trained":[43,181],"vision":[44],"encoders.":[45],"proposed":[47,92],"method,":[48,167],"GenD,":[49],"fine-tunes":[50],"only":[51],"Layer":[53],"Normalization":[54],"parameters":[55],"(0.03%":[56],"total)":[59],"and":[60,72,132,135,165],"enhances":[61],"enforcing":[64],"hyperspherical":[66],"feature":[67],"manifold":[68],"using":[69],"L2":[70],"normalization":[71],"metric":[73],"learning":[74,131],"on":[75,82,117,139,151],"it.":[76],"We":[77],"conducted":[78],"an":[79],"extensive":[80],"evaluation":[81],"14":[83],"benchmark":[84],"datasets":[85,141,154],"spanning":[86],"from":[87,121],"2019":[88],"2025.":[90],"method":[93],"achieves":[94],"state-of-the-art":[95,170],"performance,":[96],"outperforming":[97],"more":[98],"complex,":[99],"recent":[100],"in":[102],"average":[103],"cross-dataset":[104],"AUROC.":[105],"Our":[106],"analysis":[107],"yields":[108],"two":[109],"primary":[110],"findings":[111],"field:":[114],"1)":[115],"training":[116],"paired":[118],"real-fake":[119],"data":[120],"same":[123],"source":[124],"video":[125],"essential":[127],"mitigating":[129],"shortcut":[130],"improving":[133],"generalization,":[134],"2)":[136],"detection":[137],"difficulty":[138],"academic":[140],"has":[142],"not":[143],"strictly":[144],"increased":[145],"over":[146],"time,":[147],"with":[148],"trained":[150],"older,":[152],"diverse":[153],"showing":[155],"strong":[156],"capabilities.":[158],"This":[159],"delivers":[161],"computationally":[163],"efficient":[164],"reproducible":[166],"proving":[168],"attainable":[173],"making":[175],"targeted,":[176],"minimal":[177],"changes":[178],"image":[183],"encoder":[184],"model.":[185],"code":[187],"at:":[189],"https://github.com/yermandy/GenD":[190]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-17T09:13:05.818461","created_date":"2025-10-15T00:00:00"}