{"id":"https://openalex.org/W7133341761","doi":"https://doi.org/10.48550/arxiv.2603.01544","title":"RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry","display_name":"RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133341761","doi":"https://doi.org/10.48550/arxiv.2603.01544"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01544","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01544","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074191416","display_name":"X Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang, Xinchang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047428496","display_name":"Yunhao Chen","orcid":"https://orcid.org/0000-0001-7471-3271"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yunhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127906359","display_name":"Yuechen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yuechen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Bian, Congcong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bian, Congcong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127916856","display_name":"Zihao Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Zihao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127957092","display_name":"Xingjun Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Xingjun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127920307","display_name":"Hui Li (32376)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5074191416"],"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":0.48240000009536743,"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":0.48240000009536743,"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":0.16670000553131104,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.15330000221729279,"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":0.8323000073432922},{"id":"https://openalex.org/keywords/asymmetry","display_name":"Asymmetry","score":0.6410999894142151},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5342000126838684},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5227000117301941},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4569999873638153},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.44760000705718994}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8323000073432922},{"id":"https://openalex.org/C38976095","wikidata":"https://www.wikidata.org/wiki/Q752641","display_name":"Asymmetry","level":2,"score":0.6410999894142151},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6053000092506409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.597100019454956},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5227000117301941},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4569999873638153},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.44760000705718994},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3702999949455261},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35989999771118164},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2782000005245209},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.2732999920845032}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01544","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01544","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":"article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6663366556167603,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"image":[1],"generators":[2],"produce":[3],"photo-realistic":[4],"content":[5],"that":[6,23,96,129,181,193],"undermines":[7],"the":[8,156],"reliability":[9],"of":[10],"downstream":[11],"recognition":[12],"systems.":[13],"As":[14],"visual":[15],"appearance":[16,38],"cues":[17,27],"become":[18],"less":[19],"pronounced,":[20],"appearance-driven":[21],"detectors":[22],"rely":[24],"on":[25,42,116],"forensic":[26],"or":[28],"high-level":[29],"representations":[30,70],"lose":[31],"stability.":[32],"This":[33],"motivates":[34],"a":[35,59,93,98,125,134,185,202],"shift":[36],"from":[37],"to":[39,46,85,104],"behavior,":[40],"focusing":[41],"how":[43,51],"images":[44,66,77],"respond":[45],"controlled":[47],"perturbations":[48],"rather":[49],"than":[50,147],"they":[52],"look.":[53],"In":[54],"this":[55,86,102,117,199],"work,":[56],"we":[57,119],"identify":[58],"simple":[60],"and":[61,91,144,166,172,192],"universal":[62,204],"behavioral":[63],"signal.":[64,137],"Natural":[65],"preserve":[67],"stable":[68],"semantic":[69],"under":[71],"small,":[72],"structured":[73],"perturbations,":[74],"whereas":[75],"generated":[76],"exhibit":[78],"markedly":[79],"larger":[80],"feature":[81],"drift.":[82],"We":[83],"refer":[84],"phenomenon":[87],"as":[88],"robustness":[89,131,182],"asymmetry":[90,103,132,183],"provide":[92],"theoretical":[94],"analysis":[95],"establishes":[97],"lower":[99],"bound":[100],"connecting":[101],"memorization":[105],"tendencies":[106],"in":[107],"generative":[108,142],"models,":[109],"explaining":[110],"its":[111],"prevalence":[112],"across":[113,139,174],"architectures.":[114],"Building":[115],"insight,":[118],"introduce":[120],"Robustness":[121],"Asymmetry":[122],"Detection":[123],"(RA-Det),":[124],"behavior-driven":[126],"detection":[127,191],"framework":[128],"converts":[130],"into":[133,201],"reliable":[135],"decision":[136],"Evaluated":[138],"14":[140],"diverse":[141],"models":[143],"against":[145],"more":[146],"10":[148],"strong":[149],"detectors,":[150],"RA-Det":[151],"achieves":[152],"superior":[153],"performance,":[154],"improving":[155],"average":[157],"performance":[158],"by":[159],"7.81":[160],"percent.":[161],"The":[162,206],"method":[163],"is":[164,184,209],"data-":[165],"model-agnostic,":[167],"requires":[168],"no":[169],"generator":[170],"fingerprints,":[171],"transfers":[173],"unseen":[175],"generators.":[176],"Together,":[177],"these":[178],"results":[179],"indicate":[180],"stable,":[186],"general":[187],"cue":[188,200],"for":[189],"synthetic-image":[190],"carefully":[194],"designed":[195],"probing":[196],"can":[197],"turn":[198],"practical,":[203],"detector.":[205],"source":[207],"code":[208],"publicly":[210],"available":[211],"at":[212],"Github.":[213]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-03-04T00:00:00"}
