{"id":"https://openalex.org/W7164024864","doi":"https://doi.org/10.48550/arxiv.2606.07916","title":"The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence","display_name":"The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence","publication_year":2026,"publication_date":"2026-06-06","ids":{"openalex":"https://openalex.org/W7164024864","doi":"https://doi.org/10.48550/arxiv.2606.07916"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07916","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07916","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.2606.07916","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018406326","display_name":"Kelly McConvey","orcid":"https://orcid.org/0000-0002-1320-7401"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"McConvey, Kelly","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027787750","display_name":"Jalehsadat Mahdavimoghaddam","orcid":"https://orcid.org/0000-0002-8568-1006"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahdavimoghaddam, Jalehsadat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122630573","display_name":"Nima Jamali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jamali, Nima","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094208322","display_name":"Maksym Taranukhin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taranukhin, Maksym","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137858769","display_name":"Sajad Ebrahimi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ebrahimi, Sajad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138246848","display_name":"Wentao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wentao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138281362","display_name":"Yuntian Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Yuntian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037241615","display_name":"Karen Eltis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eltis, Karen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083981495","display_name":"Maura R. Grossman","orcid":"https://orcid.org/0000-0003-2279-4262"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grossman, Maura R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138232073","display_name":"Vered Shwartz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shwartz, Vered","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5064660738","display_name":"Ebrahim Bagheri","orcid":"https://orcid.org/0000-0002-5148-6237"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bagheri, Ebrahim","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/T13910","display_name":"Computational and Text Analysis Methods","score":0.07400000095367432,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.07400000095367432,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"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":0.07249999791383743,"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/T12034","display_name":"Digital and Cyber Forensics","score":0.06260000169277191,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/generative-grammar","display_name":"Generative grammar","score":0.7231000065803528},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6207000017166138},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6121000051498413},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5812000036239624},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5120999813079834},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.47290000319480896},{"id":"https://openalex.org/keywords/natural","display_name":"Natural (archaeology)","score":0.4052000045776367},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.34850001335144043}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7402999997138977},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7231000065803528},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6207000017166138},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6121000051498413},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5812000036239624},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5120999813079834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5099999904632568},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.47290000319480896},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38339999318122864},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.34850001335144043},{"id":"https://openalex.org/C139621336","wikidata":"https://www.wikidata.org/wiki/Q3190382","display_name":"Economic Justice","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32179999351501465},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3100000023841858},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C2776235265","wikidata":"https://www.wikidata.org/wiki/Q18392052","display_name":"Fragment (logic)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C2780589192","wikidata":"https://www.wikidata.org/wiki/Q7285140","display_name":"Raising (metalworking)","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2615000009536743}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07916","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07916","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.2606.07916","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07916","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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7933651208877563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,167],"growing":[1],"ability":[2],"of":[3,31,77,97,124,159,176,194],"generative":[4,196],"models":[5],"to":[6,14,74,155,183,201,219],"produce":[7],"realistic":[8,163],"documents":[9,47],"poses":[10],"a":[11,129,152,174,191],"direct":[12],"challenge":[13],"evidentiary":[15,46,126],"workflows":[16],"in":[17],"the":[18,22,29,75,86,117,142,147],"justice":[19,87,143],"system":[20,88],"and":[21,37,80,113,164,173,187,207,216],"courts,":[23],"where":[24],"decisions":[25],"increasingly":[26],"depend":[27],"on":[28,67,95,104],"authenticity":[30],"evidence":[32,160],"such":[33],"as":[34],"receipts,":[35],"communications,":[36],"administrative":[38],"records.":[39],"Unlike":[40],"social":[41,109],"media":[42,110],"or":[43,100,103,108,120],"academic":[44,107],"settings,":[45],"are":[48,92],"often":[49],"only":[50],"subtly":[51],"altered,":[52],"with":[53],"small,":[54],"localized":[55],"edits":[56,182],"that":[57],"preserve":[58],"overall":[59],"plausibility":[60],"while":[61,210],"changing":[62],"legal":[63],"meaning.":[64],"Yet":[65],"progress":[66],"automated":[68],"detection":[69,132],"remains":[70],"limited,":[71],"largely":[72],"due":[73],"absence":[76],"suitable":[78],"training":[79,215],"evaluation":[81,158],"data":[82,218],"especially":[83],"suited":[84],"for":[85,141],"requirements.":[89],"Existing":[90],"resources":[91],"either":[93],"focused":[94],"photos":[96],"human":[98],"faces":[99],"natural":[101],"scenery":[102],"narrowly":[105],"scoped":[106],"document":[111,171,185],"types,":[112],"do":[114,134],"not":[115,135],"capture":[116],"structure,":[118],"diversity,":[119],"manipulation":[121,177,205],"patterns":[122],"characteristic":[123],"real-world":[125,221],"data.":[127],"As":[128],"result,":[130],"current":[131],"systems":[133],"necessarily":[136],"learn":[137],"meaningful":[138],"signals":[139],"appropriate":[140],"system.":[144],"We":[145],"introduce":[146],"CIFAR":[148],"Synthetic":[149],"Evidence":[150],"Corpus,":[151],"dataset":[153],"designed":[154],"enable":[156],"rigorous":[157],"verification":[161],"under":[162],"controlled":[165],"conditions.":[166],"corpus":[168],"spans":[169],"multiple":[170],"families":[172],"spectrum":[175],"strategies,":[178],"from":[179],"small":[180],"field-level":[181],"complete":[184],"fabrication,":[186],"is":[188,199],"constructed":[189],"using":[190],"diverse":[192],"set":[193],"state-of-the-art":[195],"tools.":[197],"It":[198],"organized":[200],"systematically":[202],"vary":[203],"both":[204],"complexity":[206],"generation":[208],"method,":[209],"enforcing":[211],"source-level":[212],"separation":[213],"between":[214],"test":[217],"reflect":[220],"generalization":[222],"challenges.":[223]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}