{"id":"https://openalex.org/W4416035077","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.257","title":"FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter","display_name":"FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416035077","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.257"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.257","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.257","pdf_url":"https://aclanthology.org/2025.findings-emnlp.257.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.257.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110093184","display_name":"Junxi Wang","orcid":"https://orcid.org/0000-0003-4964-4440"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JunXi Wang","raw_affiliation_strings":["Hefei University of Technology , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University of Technology , China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101651167","display_name":"Yaxiong Wang","orcid":"https://orcid.org/0000-0001-6596-8117"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaxiong Wang","raw_affiliation_strings":["Hefei University of Technology , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University of Technology , China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060524512","display_name":"Lechao Cheng","orcid":"https://orcid.org/0000-0002-7546-9052"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lechao Cheng","raw_affiliation_strings":["Hefei University of Technology , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University of Technology , China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065328976","display_name":"Zhun Zhong","orcid":"https://orcid.org/0000-0002-8202-0544"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhun Zhong","raw_affiliation_strings":["Hefei University of Technology , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University of Technology , China","institution_ids":["https://openalex.org/I16365422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16365422"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4782","last_page":"4798"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.22830000519752502,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.22830000519752502,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.1168999969959259,"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":0.1128000020980835,"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/context","display_name":"Context (archaeology)","score":0.30959999561309814},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.2549000084400177},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.24969999492168427},{"id":"https://openalex.org/keywords/deception","display_name":"Deception","score":0.2402999997138977},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.23559999465942383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5552999973297119},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4677000045776367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3102000057697296},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.24969999492168427},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2475000023841858},{"id":"https://openalex.org/C2779267917","wikidata":"https://www.wikidata.org/wiki/Q170028","display_name":"Deception","level":2,"score":0.2402999997138977},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.23559999465942383},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.22830000519752502}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.257","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.257","pdf_url":"https://aclanthology.org/2025.findings-emnlp.257.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.257","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.257","pdf_url":"https://aclanthology.org/2025.findings-emnlp.257.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320326536","display_name":"Hefei University","ror":"https://ror.org/01f5rdf64"},{"id":"https://openalex.org/F4320328099","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416035077.pdf","grobid_xml":"https://content.openalex.org/works/W4416035077.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1],"FakeSV-VLM":[2],"in":[3,43,84,240],"this":[4,22,130],"paper,":[5],"a":[6,171,176,237],"new":[7,238],"VLM-based":[8],"framework":[9],"for":[10,80,170],"detecting":[11],"fake":[12,30,81,125,127,183,204],"news":[13,31,55,82,90,184,205],"on":[14,214],"short":[15,42,85,95],"video":[16,96,106,121],"platforms.Despite":[17],"significant":[18],"efforts":[19],"to":[20,25,34,48,52,137,175],"combat":[21],"issue":[23],"due":[24,47],"the":[26,54,120,152,182,195,203,208,222,241],"severe":[27],"threat":[28],"that":[29,94],"videos":[32,185],"pose":[33],"public":[35],"information":[36],"security,":[37],"existing":[38],"methods":[39],"still":[40],"fall":[41],"detection":[44,83,159],"accuracy,":[45],"often":[46,186],"lack":[49],"of":[50,89,148,224],"knowledge":[51,69],"verify":[53,221],"is":[56,124,244],"real":[57,110,112],"or":[58,114,118,122],"not.However,":[59],"large":[60],"Vision":[61],"Language":[62],"Models":[63],"(VLMs)":[64],"have":[65],"absorbed":[66],"extensive":[67],"real-world":[68],"from":[70],"massive":[71],"multimodal":[72],"datasets.Motivated":[73],"by":[74,129,167,206,232],"this,":[75],"we":[76,92,132,150,179,192],"adapt":[77],"advanced":[78],"VLMs":[79],"videos.Upon":[86],"close":[87],"examination":[88],"samples,":[91],"observe":[93],"samples":[97],"can":[98],"be":[99],"categorized":[100],"into":[101,144],"four":[102,134],"distinct":[103],"scenarios:":[104],"both":[105,115],"and":[107,141,219,234],"text":[108,123],"are":[109,116],"(for":[111,126],"samples),":[113],"fake,":[117],"either":[119],"samples).Inspired":[128],"insight,":[131],"design":[133,194],"experts":[135,160,169],"tailored":[136],"handle":[138],"each":[139],"scenario":[140],"integrate":[142],"them":[143],"VLM":[145],"via":[146],"Mixture":[147],"Experts.Specifically,":[149],"develop":[151],"Progressive":[153],"MoE":[154],"Adapter":[155],"(PMOE)":[156],"module":[157],"where":[158],"first":[161],"provide":[162],"an":[163],"initial":[164],"analysis,":[165],"followed":[166],"attribution":[168],"comprehensive":[172],"diagnosis,":[173],"leading":[174],"robust":[177],"decision.Additionally,":[178],"also":[180],"note":[181],"show":[187],"inconsistency":[188,209],"between":[189,210],"two":[190,215],"modalities.Consequently,":[191],"further":[193],"Alignment-driven":[196],"Event":[197],"Checking":[198],"(ADEC)":[199],"module,":[200],"which":[201],"perceives":[202],"capturing":[207],"different":[211],"modalities.Extensive":[212],"experiments":[213],"benchmark":[216,239],"datasets,":[217],"FakeSV":[218],"FakeTT,":[220],"superiority":[223],"our":[225],"model.It":[226],"significantly":[227],"outperforms":[228],"current":[229],"state-of-theart":[230],"models":[231],"+3.32%":[233],"+5.02%,":[235],"establishing":[236],"field.Our":[242],"code":[243],"available":[245],"at":[246],"https://github.com/Celina-love-sweet/FakeSV-VLM.":[247]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}