{"id":"https://openalex.org/W4417076538","doi":"https://doi.org/10.3389/fdata.2025.1669488","title":"CrossDF: improving cross-domain deepfake detection with deep information decomposition","display_name":"CrossDF: improving cross-domain deepfake detection with deep information decomposition","publication_year":2025,"publication_date":"2025-11-18","ids":{"openalex":"https://openalex.org/W4417076538","doi":"https://doi.org/10.3389/fdata.2025.1669488","pmid":"https://pubmed.ncbi.nlm.nih.gov/41346569"},"language":"en","primary_location":{"id":"doi:10.3389/fdata.2025.1669488","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2025.1669488","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1669488/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1669488/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103381061","display_name":"Shanmin Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]},{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanmin Yang","raw_affiliation_strings":["Computer Science and Technology, Chengdu University of Information Technology, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Technology, Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400","https://openalex.org/I31595395"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006026396","display_name":"Guo Hui","orcid":"https://orcid.org/0000-0002-6641-7410"},"institutions":[{"id":"https://openalex.org/I115441956","display_name":"Buffalo State University","ror":"https://ror.org/05ms04m92","country_code":"US","type":"education","lineage":["https://openalex.org/I115441956"]},{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Guo","raw_affiliation_strings":["University at Buffalo, State University of New York (SUNY), Buffalo, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo, State University of New York (SUNY), Buffalo, NY, United States","institution_ids":["https://openalex.org/I63190737","https://openalex.org/I115441956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100687829","display_name":"Shu Hu","orcid":"https://orcid.org/0000-0003-1446-4140"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shu Hu","raw_affiliation_strings":["Purdue University, West Lafayette, IN, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, United States","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100619754","display_name":"Bin Zhu","orcid":"https://orcid.org/0000-0002-5478-1426"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Zhu","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100738026","display_name":"Ying Fu","orcid":"https://orcid.org/0000-0002-7358-6167"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]},{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Fu","raw_affiliation_strings":["Computer Science and Technology, Chengdu University of Information Technology, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Technology, Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400","https://openalex.org/I31595395"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023752172","display_name":"Siwei Lyu","orcid":"https://orcid.org/0000-0002-0992-685X"},"institutions":[{"id":"https://openalex.org/I115441956","display_name":"Buffalo State University","ror":"https://ror.org/05ms04m92","country_code":"US","type":"education","lineage":["https://openalex.org/I115441956"]},{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siwei Lyu","raw_affiliation_strings":["University at Buffalo, State University of New York (SUNY), Buffalo, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo, State University of New York (SUNY), Buffalo, NY, United States","institution_ids":["https://openalex.org/I63190737","https://openalex.org/I115441956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101608091","display_name":"Xi Wu","orcid":"https://orcid.org/0000-0002-0689-1735"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]},{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xi Wu","raw_affiliation_strings":["Computer Science and Technology, Chengdu University of Information Technology, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Technology, Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400","https://openalex.org/I31595395"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100327844","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0001-8639-3818"},"institutions":[{"id":"https://openalex.org/I113508548","display_name":"Albany State University","ror":"https://ror.org/01vme4277","country_code":"US","type":"education","lineage":["https://openalex.org/I113508548"]},{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xin Wang","raw_affiliation_strings":["University at Albany, State University of New York (SUNY), Albany, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Albany, State University of New York (SUNY), Albany, NY, United States","institution_ids":["https://openalex.org/I392282","https://openalex.org/I113508548"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100327844","https://openalex.org/A5101608091"],"corresponding_institution_ids":["https://openalex.org/I113508548","https://openalex.org/I24201400","https://openalex.org/I31595395","https://openalex.org/I392282"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":2.0087,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.89356749,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"1669488","last_page":"1669488"},"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":0.8382999897003174,"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":0.8382999897003174,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.021700000390410423,"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/T11448","display_name":"Face recognition and analysis","score":0.020600000396370888,"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/robustness","display_name":"Robustness (evolution)","score":0.6500999927520752},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.512499988079071},{"id":"https://openalex.org/keywords/decorrelation","display_name":"Decorrelation","score":0.5013999938964844},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4577000141143799},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.37929999828338623},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.36480000615119934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7505000233650208},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6500999927520752},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6337000131607056},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5076000094413757},{"id":"https://openalex.org/C177860922","wikidata":"https://www.wikidata.org/wiki/Q788608","display_name":"Decorrelation","level":2,"score":0.5013999938964844},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4577000141143799},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.36480000615119934},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31769999861717224},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2815999984741211}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3389/fdata.2025.1669488","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2025.1669488","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1669488/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},{"id":"pmid:41346569","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41346569","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in big data","raw_type":null},{"id":"pmh:oai:doaj.org/article:348b1542b62d4916882f6fd23687afab","is_oa":true,"landing_page_url":"https://doaj.org/article/348b1542b62d4916882f6fd23687afab","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":"Frontiers in Big Data, Vol 8 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fdata.2025.1669488","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2025.1669488","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1669488/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417076538.pdf"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W107619411","https://openalex.org/W2056716515","https://openalex.org/W2118978333","https://openalex.org/W2149350210","https://openalex.org/W2341528187","https://openalex.org/W2486034530","https://openalex.org/W2737047298","https://openalex.org/W2942074357","https://openalex.org/W2963968629","https://openalex.org/W3040168631","https://openalex.org/W3205301887","https://openalex.org/W4210623456","https://openalex.org/W4281632540","https://openalex.org/W4288102863","https://openalex.org/W4382318331","https://openalex.org/W4386275888","https://openalex.org/W4387967853","https://openalex.org/W4390414869","https://openalex.org/W4390873235","https://openalex.org/W4391020241"],"related_works":[],"abstract_inverted_index":{"Deepfake":[0,66],"technology":[1],"represents":[2],"a":[3,57],"serious":[4],"risk":[5],"to":[6,40,63,141,149,181,191],"safety":[7],"and":[8,30,98,110,144,159,183],"public":[9],"confidence.":[10],"While":[11],"current":[12],"detection":[13,74],"approaches":[14],"perform":[15],"well":[16],"in":[17,36,176],"identifying":[18],"manipulations":[19],"within":[20],"datasets":[21],"that":[22,122],"utilize":[23],"identical":[24],"deepfake":[25,45,73,168],"methods":[26],"for":[27,106,166],"both":[28],"training":[29],"validation,":[31],"they":[32],"experience":[33],"notable":[34],"declines":[35],"accuracy":[37],"when":[38],"applied":[39],"cross-dataset":[41,167,177],"situations,":[42],"where":[43],"unfamiliar":[44],"techniques":[46],"are":[47],"encountered":[48],"during":[49],"testing.":[50],"To":[51],"tackle":[52],"this":[53],"issue,":[54],"we":[55,114],"propose":[56],"Deep":[58],"Information":[59],"Decomposition":[60],"(DID)":[61],"framework":[62,77,165],"improve":[64],"Cross-dataset":[65],"Detection":[67],"(CrossDF).":[68],"Distinct":[69],"from":[70,179,189],"most":[71],"existing":[72],"approaches,":[75],"our":[76,162],"emphasizes":[78],"high-level":[79],"semantic":[80],"attributes":[81],"instead":[82],"of":[83,130,161,174],"focusing":[84],"on":[85,193],"particular":[86],"visual":[87],"anomalies.":[88],"More":[89],"specifically,":[90],"it":[91],"intrinsically":[92],"decomposes":[93],"facial":[94],"representations":[95],"into":[96],"deepfake-relevant":[97,104],"unrelated":[99],"components,":[100],"leveraging":[101],"only":[102],"the":[103,124,138,157,185,194],"features":[105],"classification":[107],"between":[108,126],"genuine":[109],"fabricated":[111],"images.":[112],"Furthermore,":[113],"introduce":[115],"an":[116,172],"adversarial":[117],"mutual":[118],"information":[119,131],"minimization":[120],"strategy":[121],"enhances":[123],"separability":[125],"these":[127],"two":[128],"types":[129],"through":[132],"decorrelation":[133],"learning.":[134],"This":[135],"significantly":[136,188],"improves":[137,184],"model's":[139],"robustness":[140],"irrelevant":[142],"variations":[143],"strengthens":[145],"its":[146],"generalization":[147],"capability":[148],"previously":[150],"unseen":[151],"manipulation":[152],"techniques.":[153],"Extensive":[154],"experiments":[155],"demonstrate":[156],"effectiveness":[158],"superiority":[160],"proposed":[163],"DID":[164],"detection.":[169],"It":[170],"achieves":[171],"AUC":[173,187],"0.779":[175],"evaluation":[178],"FF++":[180],"CDF2":[182],"state-of-the-art":[186],"0.669":[190],"0.802":[192],"diffusion-based":[195],"Text-to-Image":[196],"dataset.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-11-19T00:00:00"}