{"id":"https://openalex.org/W7151483742","doi":"https://doi.org/10.48550/arxiv.2604.03558","title":"LOGER: Local--Global Ensemble for Robust Deepfake Detection in the Wild","display_name":"LOGER: Local--Global Ensemble for Robust Deepfake Detection in the Wild","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151483742","doi":"https://doi.org/10.48550/arxiv.2604.03558"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03558","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03558","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.03558","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133068856","display_name":"Fei Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113405461","display_name":"Dagong Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Dagong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064996014","display_name":"Mufeng Yao","orcid":"https://orcid.org/0009-0008-5047-5158"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Mufeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133084534","display_name":"Xinlei Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Xinlei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133114309","display_name":"Fengjun Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Fengjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.6872000098228455,"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":0.6872000098228455,"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":0.1168999969959259,"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/T11019","display_name":"Image Enhancement Techniques","score":0.058800000697374344,"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":0.6373999714851379},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5735999941825867},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.47360000014305115},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4629000127315521},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40779998898506165},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3781000077724457},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.3668000102043152},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.3621000051498413}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7027999758720398},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6373999714851379},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5735999941825867},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5670999884605408},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.47360000014305115},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4629000127315521},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43939998745918274},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40779998898506165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.382999986410141},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.28679999709129333},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03558","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03558","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.03558","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03558","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6382462978363037,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Robust":[0,76,180],"deepfake":[1,23,77],"detection":[2,24],"in":[3,32,46,152,176],"the":[4,10,117,126,135,148,177],"wild":[5],"remains":[6],"challenging":[7],"due":[8],"to":[9,91],"ever-growing":[11],"variety":[12],"of":[13,128],"manipulation":[14,199],"techniques":[15],"and":[16,34,41,139,155,184,195,201],"uncontrolled":[17],"real-world":[18,202],"degradations.":[19],"Forensic":[20],"cues":[21],"for":[22,75,168],"reside":[25],"at":[26,88,133],"two":[27,149],"complementary":[28],"levels:":[29],"global-level":[30],"anomalies":[31,94],"semantics":[33],"statistics":[35],"that":[36,49,113,164],"require":[37],"holistic":[38,93],"image":[39,137],"understanding,":[40],"local-level":[42],"forgery":[43],"traces":[44],"concentrated":[45],"manipulated":[47],"regions":[48],"are":[50,159],"easily":[51],"diluted":[52],"by":[53,125],"global":[54,80],"averaging.":[55],"Since":[56],"no":[57],"single":[58],"backbone":[59],"or":[60],"input":[61],"scale":[62],"can":[63],"effectively":[64],"cover":[65],"both":[66,134,153],"levels,":[67],"we":[68],"propose":[69],"LOGER,":[70],"a":[71,106,162],"LOcal--Global":[72],"Ensemble":[73],"framework":[74],"detection.":[78],"The":[79,99],"branch":[81,101],"employs":[82],"heterogeneous":[83],"vision":[84],"foundation":[85],"model":[86],"backbones":[87],"multiple":[89,188],"resolutions":[90],"capture":[92],"with":[95,105],"diverse":[96,198],"visual":[97],"priors.":[98],"local":[100,144],"performs":[102],"patch-level":[103],"modeling":[104],"Multiple":[107],"Instance":[108],"Learning":[109],"top-$k$":[110],"aggregation":[111],"strategy":[112],"selectively":[114],"pools":[115],"only":[116],"most":[118],"suspicious":[119],"regions,":[120],"mitigating":[121],"evidence":[122],"dilution":[123],"caused":[124],"dominance":[127],"normal":[129],"patches;":[130],"dual-level":[131],"supervision":[132],"aggregated":[136],"level":[138,142],"individual":[140],"patch":[141],"keeps":[143],"responses":[145],"discriminative.":[146],"Because":[147],"branches":[150],"differ":[151],"granularity":[154],"backbone,":[156],"their":[157],"errors":[158],"largely":[160],"decorrelated,":[161],"property":[163],"logit-space":[165],"fusion":[166],"exploits":[167],"more":[169],"robust":[170],"prediction.":[171],"LOGER":[172],"achieves":[173],"2nd":[174],"place":[175],"NTIRE":[178],"2026":[179],"Deepfake":[181],"Detection":[182],"Challenge,":[183],"further":[185],"evaluation":[186],"on":[187],"public":[189],"benchmarks":[190],"confirms":[191],"its":[192],"strong":[193],"robustness":[194],"generalization":[196],"across":[197],"methods":[200],"degradation":[203],"conditions.":[204]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}