{"id":"https://openalex.org/W4417073373","doi":"https://doi.org/10.1145/3743093.3771003","title":"Frequency-Enhanced Multi-Modal Consistency Learning for Audio-Visual Deepfake Detection","display_name":"Frequency-Enhanced Multi-Modal Consistency Learning for Audio-Visual Deepfake Detection","publication_year":2025,"publication_date":"2025-12-06","ids":{"openalex":"https://openalex.org/W4417073373","doi":"https://doi.org/10.1145/3743093.3771003"},"language":null,"primary_location":{"id":"doi:10.1145/3743093.3771003","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3743093.3771003","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3743093.3771003","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3743093.3771003","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xinyu Zheng","orcid":"https://orcid.org/0009-0001-8307-2821"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Zheng","raw_affiliation_strings":["Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0001-8307-2821","affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092271594","display_name":"Dongdong Zhang","orcid":"https://orcid.org/0009-0008-7218-8133"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongdong Zhang","raw_affiliation_strings":["Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0008-7218-8133","affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023062351","display_name":"Chengyu Sun","orcid":"https://orcid.org/0000-0002-5686-5957"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengyu Sun","raw_affiliation_strings":["Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5686-5957","affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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":0.8381999731063843,"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":0.8381999731063843,"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":0.053700000047683716,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.011599999852478504,"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/consistency","display_name":"Consistency (knowledge bases)","score":0.6840999722480774},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6722000241279602},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.5548999905586243},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.47850000858306885},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.391400009393692},{"id":"https://openalex.org/keywords/consistency-model","display_name":"Consistency model","score":0.3188999891281128}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7882000207901001},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6840999722480774},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6722000241279602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5619999766349792},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.5548999905586243},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4810999929904938},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4323999881744385},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C37279795","wikidata":"https://www.wikidata.org/wiki/Q2492305","display_name":"Consistency model","level":3,"score":0.3188999891281128},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3743093.3771003","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3743093.3771003","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3743093.3771003","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3743093.3771003","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3743093.3771003","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3743093.3771003","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4417073373.pdf"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W2891145043","https://openalex.org/W2963155035","https://openalex.org/W2963684180","https://openalex.org/W2971144153","https://openalex.org/W2982058372","https://openalex.org/W3003301247","https://openalex.org/W3093010840","https://openalex.org/W3093077034","https://openalex.org/W3094728142","https://openalex.org/W3096831136","https://openalex.org/W3175342695","https://openalex.org/W4214684483","https://openalex.org/W4214691743","https://openalex.org/W4312388562","https://openalex.org/W4312472072","https://openalex.org/W4313127140","https://openalex.org/W4319978495","https://openalex.org/W4360993864","https://openalex.org/W4385801058","https://openalex.org/W4386075688","https://openalex.org/W4386075954","https://openalex.org/W4386076652","https://openalex.org/W4386928847","https://openalex.org/W4387540674","https://openalex.org/W4393154086","https://openalex.org/W4400975066","https://openalex.org/W4402774452","https://openalex.org/W4403791323"],"related_works":[],"abstract_inverted_index":{"The":[0,76],"rapid":[1],"advancement":[2],"of":[3,82,154],"DeepFake":[4],"technology":[5],"poses":[6],"a":[7,49,106,111,142,150],"formidable":[8],"challenge":[9],"to":[10,72,96],"information":[11,39],"security.":[12],"Existing":[13],"multimodal":[14],"detection":[15],"methods":[16],"have":[17],"achieved":[18],"success":[19],"by":[20,90,139,146],"examining":[21],"audio-visual":[22],"consistency,":[23],"but":[24],"they":[25],"primarily":[26],"model":[27],"features":[28],"in":[29,40,61],"the":[30,37,41,118,125],"spatial":[31],"or":[32],"high-level":[33],"semantic":[34],"domains,":[35],"overlooking":[36],"rich":[38],"temporal-frequency":[42],"domain.":[43],"To":[44],"address":[45],"this,":[46],"we":[47],"propose":[48],"novel,":[50],"plug-and-play":[51],"framework":[52,77],"named":[53],"FEMC":[54,59,126],"(Frequency-Enhanced":[55],"Multi-Modal":[56],"Consistency":[57],"Learning).":[58],"operates":[60],"parallel":[62],"with":[63],"arbitrary":[64],"base":[65,134],"models":[66,79],"and":[67,110,141],"introduces":[68],"frequency-domain":[69,97],"analysis":[70],"branches":[71],"capture":[73],"signal-level":[74],"inconsistencies.":[75],"jointly":[78],"two":[80],"levels":[81],"consistency.":[83],"First,":[84],"it":[85,100],"learns":[86],"intra-modal":[87],"temporal":[88,103],"coherence":[89],"applying":[91],"an":[92],"enhanced":[93],"attention":[94,108],"mechanism":[95],"representations.":[98],"Second,":[99],"enforces":[101],"inter-modal":[102],"correspondence":[104],"through":[105],"cross-modal":[107],"module":[109,127],"dedicated":[112],"contrastive":[113],"loss.":[114],"Extensive":[115],"experiments":[116],"on":[117],"dataset":[119],"FakeAVCeleb":[120],"validate":[121],"our":[122],"approach.":[123],"Integrating":[124],"yields":[128],"significant":[129],"performance":[130],"gains":[131],"across":[132],"diverse":[133],"models,":[135],"boosting":[136],"AV-HuBERT\u2019s":[137],"accuracy":[138],"5.39%":[140],"combined":[143],"R(2+1)D+VGGish":[144],"model\u2019s":[145],"3.08%,":[147],"while":[148],"establishing":[149],"new":[151],"state-of-the-art":[152],"AUC":[153],"99.76%.":[155]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-06T00:00:00"}
