{"id":"https://openalex.org/W7125914367","doi":"https://doi.org/10.1145/3789507","title":"A Multi-Grained Parallel Spatio-Temporal Learning Architecture for Deepfake Video Detection","display_name":"A Multi-Grained Parallel Spatio-Temporal Learning Architecture for Deepfake Video Detection","publication_year":2026,"publication_date":"2026-01-28","ids":{"openalex":"https://openalex.org/W7125914367","doi":"https://doi.org/10.1145/3789507"},"language":"en","primary_location":{"id":"doi:10.1145/3789507","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3789507","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124112404","display_name":"Hui Miao","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Miao","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-0960-0581","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuanfang Guo","orcid":"https://orcid.org/0000-0003-4592-8083"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanfang Guo","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4592-8083","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121758839","display_name":"Leo Yu Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Leo Yu Zhang","raw_affiliation_strings":["School of Information and Communication Technology, Griffith University, Gold Coast, Australia"],"raw_orcid":"https://orcid.org/0000-0001-9330-2662","affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, Griffith University, Gold Coast, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124076342","display_name":"Jiantao Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Jiantao Zhou","raw_affiliation_strings":["Department of Computer and Information Science, University of Macau, Macau, China"],"raw_orcid":"https://orcid.org/0000-0002-6015-2618","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, University of Macau, Macau, China","institution_ids":["https://openalex.org/I204512498"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124065826","display_name":"Yunhong Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhong Wang","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8001-2703","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"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":0.09300617,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":"4","first_page":"1","last_page":"24"},"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":0.763700008392334,"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":0.763700008392334,"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":0.11330000311136246,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.02810000069439411,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7254999876022339},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5906000137329102},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5534999966621399},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.48579999804496765},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4244999885559082},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4115000069141388},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.3804999887943268},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.36890000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9247999787330627},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7254999876022339},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5906000137329102},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5622000098228455},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5534999966621399},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5220000147819519},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.48579999804496765},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3736000061035156},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3091000020503998},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2939999997615814},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3789507","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3789507","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7464357614517212,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2586418231","display_name":null,"funder_award_id":"No. F2024202047","funder_id":"https://openalex.org/F4320322163","funder_display_name":"Natural Science Foundation of Hebei Province"},{"id":"https://openalex.org/G3882625959","display_name":null,"funder_award_id":"MYRG-CRG2025-00031-FST and MYRG-GRG2025-00086-FST","funder_id":"https://openalex.org/F4320322841","funder_display_name":"Universidade de Macau"},{"id":"https://openalex.org/G4651853227","display_name":null,"funder_award_id":"2024A1515012536","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G5196642116","display_name":null,"funder_award_id":"62272020","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322163","display_name":"Natural Science Foundation of Hebei Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322841","display_name":"Universidade de Macau","ror":"https://ror.org/01r4q9n85"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2942074357","https://openalex.org/W3108281670","https://openalex.org/W4280579728","https://openalex.org/W4317796367","https://openalex.org/W4399569783","https://openalex.org/W4402666392"],"related_works":[],"abstract_inverted_index":{"With":[0],"advances":[1],"in":[2,29],"generation":[3],"techniques,":[4],"malicious":[5],"users":[6],"can":[7,13],"easily":[8],"generate":[9],"deepfake":[10,22,84,97,175,200,225],"videos,":[11],"which":[12,88,156],"cause":[14],"severe":[15],"social":[16],"problems":[17],"and":[18,39,69,121,132,152,164,193,212,232],"trust":[19],"issues.":[20],"Therefore,":[21],"video":[23,85],"detection":[24,42,86],"has":[25],"received":[26],"increasing":[27],"attention":[28],"recent":[30],"years.":[31],"Given":[32],"that":[33,206],"forgery":[34],"clues":[35],"are":[36],"often":[37],"subtle":[38,199],"imperceptible,":[40],"effective":[41],"relies":[43],"heavily":[44],"on":[45,234],"multi-grained":[46,55,81,137,181],"learning.":[47,140,202],"However,":[48],"existing":[49],"approaches":[50],"fail":[51],"to":[52,93,115,135,172],"systematically":[53],"incorporate":[54],"learning":[56],"across":[57,221],"the":[58,64,100,127,169,213,217],"key":[59],"components":[60],"of":[61,224],"network":[62,67,109],"training\u2014namely,":[63],"training":[65,101,158],"data,":[66],"structure,":[68],"supervision":[70],"strategy\u2014thus":[71],"limiting":[72],"their":[73],"performance.":[74],"In":[75],"this":[76],"article,":[77],"we":[78,104,142,178],"propose":[79,143],"a":[80,90,106,112,180,184,188,194,222],"parallel":[82,107],"spatio-temporal":[83,108,138,153],"architecture,":[87],"introduces":[89],"novel":[91],"framework":[92],"mine":[94],"more":[95],"discriminative":[96],"cues":[98],"throughout":[99],"pipeline.":[102],"Firstly,":[103],"design":[105],"combined":[110],"with":[111],"cross-guided":[113],"mechanism":[114],"concurrently":[116],"extract":[117],"frame-level":[118,190],"spatial":[119,130,191],"features":[120],"patch-level":[122,185],"temporal":[123,133,186],"features,":[124],"while":[125],"leveraging":[126],"relationship":[128],"between":[129],"artifacts":[131],"inconsistencies":[134],"enable":[136],"synchronous":[139],"Secondly,":[141],"segment-level":[144,196],"data":[145,154,159],"augmentation":[146],"strategies,":[147],"including":[148,227],"frame-random":[149],"consistent":[150],"self-blending":[151],"augmentation,":[155],"improve":[157],"diversity":[160],"at":[161],"both":[162],"frame":[163],"patch":[165],"levels,":[166],"thereby":[167],"improving":[168],"model\u2019s":[170],"ability":[171,215],"learn":[173],"comprehensive":[174],"representations.":[176],"Finally,":[177],"construct":[179],"supervision,":[182],"comprising":[183],"loss,":[187,192,197],"distance-based":[189],"standard":[195],"for":[198],"feature":[201],"Extensive":[203],"experiments":[204],"demonstrate":[205],"our":[207],"method":[208],"possesses":[209],"strong":[210],"robustness":[211],"generalization":[214],"outperforms":[216],"current":[218],"state-of-the-art":[219],"methods":[220],"series":[223],"datasets,":[226],"FaceForensics++,":[228],"CelebDF,":[229],"DFDC,":[230],"DeeperForensics,":[231],"Faceshifter,":[233],"average.":[235]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-01-29T00:00:00"}