{"id":"https://openalex.org/W7131378990","doi":"https://doi.org/10.48550/arxiv.2602.20412","title":"SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images","display_name":"SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131378990","doi":"https://doi.org/10.48550/arxiv.2602.20412"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.20412","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108919727","display_name":"Aayush Dhakal","orcid":"https://orcid.org/0000-0003-4431-0628"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dhakal, Aayush","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038402302","display_name":"Subash Khanal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khanal, Subash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089378935","display_name":"Srikumar Sastry","orcid":"https://orcid.org/0000-0002-4646-9416"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sastry, Srikumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029264783","display_name":"Jacob Arndt","orcid":"https://orcid.org/0000-0002-1097-0428"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arndt, Jacob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109055074","display_name":"Philipe Ambrozio Dias","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dias, Philipe Ambrozio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083948807","display_name":"Dalton Lunga","orcid":"https://orcid.org/0000-0003-0054-1141"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lunga, Dalton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5029557305","display_name":"Nathan Jacobs","orcid":"https://orcid.org/0000-0002-4242-8967"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jacobs, Nathan","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21769966,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.6869999766349792,"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":0.6869999766349792,"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":0.061400000005960464,"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"}},{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.05339999869465828,"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":0.7684999704360962},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.559499979019165},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.501800000667572},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.426800012588501},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3887999951839447},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3804999887943268},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.37700000405311584}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7684999704360962},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819999814033508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6583999991416931},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.559499979019165},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.501800000667572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.426800012588501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3887999951839447},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3856000006198883},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C180462255","wikidata":"https://www.wikidata.org/wiki/Q3559736","display_name":"Standard test image","level":4,"score":0.3422999978065491},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2831000089645386},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.27880001068115234},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.2572999894618988}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.20412","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.20412","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20412","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.20412","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7349184155464172}],"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],"rapid":[1],"advancement":[2],"of":[3,10,127],"generative":[4],"models":[5,159],"has":[6],"made":[7],"the":[8,68,74,116,136],"detection":[9,30,95],"AI-generated":[11],"images":[12],"a":[13,63,78,87],"critical":[14],"challenge":[15],"for":[16,92,138],"both":[17],"research":[18],"and":[19,37,72,89,112,148,158,165],"society.":[20],"Recent":[21],"works":[22],"have":[23],"shown":[24],"that":[25,56],"most":[26],"state-of-the-art":[27],"fake":[28,75,93,142],"image":[29,70,94,143],"methods":[31],"overfit":[32],"to":[33,61,109,151],"their":[34],"training":[35,125],"data":[36],"catastrophically":[38],"fail":[39],"when":[40],"evaluated":[41],"on":[42,115,163],"curated":[43],"hard":[44],"test":[45],"sets":[46],"with":[47],"strong":[48],"distribution":[49,71],"shifts.":[50],"In":[51],"this":[52,82],"work,":[53],"we":[54,84,134],"argue":[55],"it":[57],"is":[58,121],"more":[59],"principled":[60],"learn":[62],"tight":[64],"decision":[65],"boundary":[66],"around":[67],"real":[69],"treat":[73],"category":[76],"as":[77],"sink":[79],"class.":[80],"To":[81],"end,":[83],"propose":[85],"SimLBR,":[86],"simple":[88],"efficient":[90],"framework":[91],"using":[96],"Latent":[97],"Blending":[98],"Regularization":[99],"(LBR).":[100],"Our":[101],"method":[102],"significantly":[103],"improves":[104],"cross-generator":[105],"generalization,":[106],"achieving":[107],"up":[108],"+24.85\\%":[110],"accuracy":[111],"+69.62\\%":[113],"recall":[114],"challenging":[117],"Chameleon":[118],"benchmark.":[119],"SimLBR":[120],"also":[122],"highly":[123],"efficient,":[124],"orders":[126],"magnitude":[128],"faster":[129],"than":[130],"existing":[131],"approaches.":[132],"Furthermore,":[133],"emphasize":[135],"need":[137],"reliability-oriented":[139],"evaluation":[140],"in":[141],"detection,":[144],"introducing":[145],"risk-adjusted":[146],"metrics":[147],"worst-case":[149],"estimates":[150],"better":[152],"assess":[153],"model":[154],"robustness.":[155],"All":[156],"code":[157],"will":[160],"be":[161],"released":[162],"HuggingFace":[164],"GitHub.":[166]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-26T00:00:00"}
