{"id":"https://openalex.org/W7160525870","doi":"https://doi.org/10.48550/arxiv.2605.04445","title":"LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection","display_name":"LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160525870","doi":"https://doi.org/10.48550/arxiv.2605.04445"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04445","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04445","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.04445","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135632331","display_name":"Yutong Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Yutong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135635044","display_name":"Ran Ran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ran, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135550017","display_name":"Jiwei Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Jiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135612017","display_name":"Shuchang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Shuchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135560021","display_name":"Ke Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115560908","display_name":"Zheng Ziqiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ziqiang, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135626420","display_name":"Caiyan Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Caiyan","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/T12357","display_name":"Digital Media Forensic Detection","score":0.5849999785423279,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.5849999785423279,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.09009999781847,"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":0.07779999822378159,"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/overfitting","display_name":"Overfitting","score":0.8575000166893005},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.5677000284194946},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4521999955177307},{"id":"https://openalex.org/keywords/artifact","display_name":"Artifact (error)","score":0.4424999952316284},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41190001368522644},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4059999883174896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.382999986410141},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3587000072002411}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8575000166893005},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8137000203132629},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6808000206947327},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5677000284194946},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4521999955177307},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.4424999952316284},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41190001368522644},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3977000117301941},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C2989087649","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Image synthesis","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04445","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04445","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.04445","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04445","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,88],"rapid":[1],"advancement":[2],"of":[3,47,52,91,110,174,220],"generative":[4],"technologies":[5],"has":[6],"made":[7],"synthetic":[8],"images":[9],"nearly":[10],"indistinguishable":[11],"from":[12,178],"real":[13],"ones,":[14],"thereby":[15],"creating":[16],"an":[17,94],"urgent":[18],"need":[19],"for":[20,194],"robust":[21,182],"detectors":[22],"to":[23,71,74,96,105,133,155,169,196],"counter":[24],"misinformation.":[25],"However,":[26],"existing":[27],"methods":[28,121,210],"mainly":[29],"rely":[30],"on":[31,67,151,166],"universal":[32,126],"artifact":[33,131],"features":[34,55],"that":[35,43,122],"are":[36,164],"shared":[37],"across":[38],"multiple":[39,98],"generators.":[40],"We":[41],"observe":[42],"as":[44],"the":[45,50,107,172],"diversity":[46],"generators":[48],"increases,":[49],"overlap":[51],"these":[53],"common":[54],"gradually":[56],"decreases.":[57],"This":[58],"severely":[59],"undermines":[60],"model":[61],"generalization.":[62],"In":[63],"contrast,":[64],"focusing":[65],"only":[66,225],"unique":[68,108,130],"artifacts":[69,109],"tends":[70],"cause":[72],"overfitting":[73],"specific":[75,112],"forgery":[76],"patterns.":[77],"To":[78],"address":[79],"this":[80],"challenge,":[81],"we":[82],"propose":[83],"LEGO":[84,92,128,184],"(LoRA-Enabled":[85],"Generator-Oriented":[86],"Framework).":[87],"core":[89],"mechanism":[90],"employs":[93],"MLP":[95,160],"modulate":[97],"LoRA":[99,135,146,192],"(Low-Rank":[100],"Adaptation)":[101],"blocks,":[102],"each":[103,175,229],"pretrained":[104],"capture":[106],"a":[111,124,152],"generator,":[113],"followed":[114],"by":[115,137,189],"attention-based":[116],"feature":[117],"fusion.":[118],"Unlike":[119],"conventional":[120],"seek":[123],"single":[125],"solution,":[127],"delegates":[129],"extraction":[132],"specialized":[134],"modules":[136,193],"dividing":[138],"its":[139,179],"training":[140,215,222,230],"procedure":[141],"into":[142],"two":[143],"stages.":[144],"Each":[145],"module":[147],"is":[148],"individually":[149],"trained":[150,165],"single-generator":[153],"dataset":[154],"learn":[156],"generator-specific":[157],"representations,":[158],"then":[159],"and":[161,224],"attention":[162],"layers":[163],"mixed":[167],"datasets":[168],"dynamically":[170],"regulate":[171],"contribution":[173],"module.":[176],"Benefiting":[177],"modular":[180],"yet":[181],"design,":[183],"can":[185],"be":[186],"naturally":[187],"extended":[188],"incorporating":[190],"new":[191],"adaptation":[195],"newly":[197],"emerging":[198],"next-generation":[199],"datasets,":[200],"while":[201],"still":[202],"achieving":[203],"substantially":[204],"better":[205],"performance":[206],"than":[207,213,218],"prior":[208],"SOTA":[209],"with":[211],"fewer":[212],"30,000":[214],"images,":[216],"less":[217],"10%":[219],"their":[221],"data,":[223],"5":[226],"epochs":[227],"in":[228],"stage.":[231]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-08T00:00:00"}
