{"id":"https://openalex.org/W7134931776","doi":"https://doi.org/10.48550/arxiv.2603.09242","title":"When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection","display_name":"When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7134931776","doi":"https://doi.org/10.48550/arxiv.2603.09242"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.09242","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09242","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.2603.09242","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004439487","display_name":"Chao Shuai","orcid":"https://orcid.org/0000-0002-4858-4602"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuai, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101715620","display_name":"Zhenguang Liu","orcid":"https://orcid.org/0000-0002-7981-9873"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Shaojing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100777508","display_name":"Shaojing Fan","orcid":"https://orcid.org/0000-0002-7744-1133"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Chenlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079312541","display_name":"Bin Gong","orcid":"https://orcid.org/0000-0001-9820-4762"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128762826","display_name":"Weichen Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lian, Weichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128754306","display_name":"Xiuli Bi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bi, Xiuli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128779733","display_name":"Zhongjie Ba","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhenguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128787149","display_name":"Kui Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ba, Zhongjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Ren, Kui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Kui","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":0.8472999930381775,"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":0.8472999930381775,"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":0.09309999644756317,"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":0.009700000286102295,"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":0.7330999970436096},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5480999946594238},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5153999924659729},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4668000042438507},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.435699999332428},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42590001225471497},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.38769999146461487},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3779999911785126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7811999917030334},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7330999970436096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.646399974822998},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5480999946594238},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5153999924659729},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4668000042438507},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.435699999332428},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42590001225471497},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4106000065803528},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3889000117778778},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C144745244","wikidata":"https://www.wikidata.org/wiki/Q4927286","display_name":"Blocking (statistics)","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C57654395","wikidata":"https://www.wikidata.org/wiki/Q1097775","display_name":"Compression artifact","level":5,"score":0.33730000257492065},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.314300000667572},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C2983589003","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Text detection","level":3,"score":0.2994999885559082},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.2621999979019165}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.09242","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09242","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.2603.09242","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09242","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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6881105899810791}],"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],"growing":[1],"realism":[2],"of":[3],"generative":[4],"models":[5],"has":[6],"blurred":[7],"the":[8,49,66,70,116,128,136],"boundary":[9],"between":[10],"real":[11],"and":[12,143,176,186,189],"synthetic":[13],"content,":[14],"posing":[15],"significant":[16],"challenges":[17],"to":[18,35,63,114],"reliable":[19],"AI-generated":[20],"image":[21],"detection.":[22],"Although":[23],"large-scale":[24,175],"pre-trained":[25],"Vision":[26],"Foundation":[27],"Models":[28],"have":[29],"advanced":[30],"detection":[31],"capability,":[32],"their":[33],"generalization":[34],"images":[36],"from":[37],"unseen":[38],"generation":[39],"pipelines":[40],"remains":[41],"inadequate.":[42],"In":[43],"this":[44,86],"paper,":[45],"we":[46,88,179],"identify,":[47],"for":[48],"first":[50],"time,":[51],"a":[52,90,110,132,148,159,198],"key":[53],"failure":[54],"mechanism,":[55],"termed":[56],"\\emph{semantic":[57],"fallback},":[58],"wherein":[59],"forensic":[60,82,105],"fine-tuning":[61],"fails":[62],"fully":[64],"reshape":[65],"representation":[67],"space.":[68],"Consequently,":[69],"resulting":[71],"representations":[72],"remain":[73],"organized":[74],"along":[75],"high-level":[76],"semantic":[77,118,129,195],"structures":[78],"rather":[79],"than":[80],"manipulation-specific":[81],"cues.":[83],"Building":[84],"on":[85],"insight,":[87],"propose":[89],"\\textbf{Geometric":[91],"Semantic":[92],"Decoupling":[93],"(GSD)}":[94],"framework,":[95],"which":[96],"explicitly":[97],"suppresses":[98,127],"semantically":[99],"dominant":[100,117],"directions,":[101],"thereby":[102],"promoting":[103],"invariant":[104],"representations.":[106],"Specifically,":[107],"GSD":[108],"leverages":[109],"frozen":[111],"CLIP":[112],"encoder":[113],"estimate":[115],"subspace":[119,155],"via":[120],"Singular":[121],"Value":[122],"Decomposition":[123],"(SVD).":[124],"It":[125],"then":[126],"components":[130],"through":[131],"geometry-constrained":[133],"formulation":[134],"with":[135],"suppression":[137],"strength":[138],"adaptively":[139],"modulated":[140],"across":[141],"samples":[142],"layers.":[144],"We":[145],"further":[146],"introduce":[147],"mini-batch":[149],"SVD":[150],"approximation":[151],"strategy":[152],"that":[153,191],"amortizes":[154],"estimation,":[156],"achieving":[157],"over":[158],"$15":[160],"\\times$":[161],"reduction":[162],"in":[163],"computational":[164],"overhead":[165],"while":[166],"preserving":[167],"effectiveness.":[168],"Finally,":[169],"considering":[170],"practical":[171],"scenarios":[172],"spanning":[173],"both":[174],"online":[177],"evaluation,":[178],"develop":[180],"three":[181],"inference":[182],"protocols,":[183],"batch,":[184],"per-sample,":[185],"reference-based":[187],"inference,":[188],"demonstrate":[190],"they":[192],"induce":[193],"consistent":[194],"decoupling,":[196],"yielding":[197],"stable":[199],"forgery-oriented":[200],"feature":[201],"manifold.":[202]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-12T00:00:00"}