{"id":"https://openalex.org/W7160828652","doi":"https://doi.org/10.48550/arxiv.2605.07074","title":"Decoupling Semantics and Fingerprints: A Universal Representation for AI-Generated Image Detection","display_name":"Decoupling Semantics and Fingerprints: A Universal Representation for AI-Generated Image Detection","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160828652","doi":"https://doi.org/10.48550/arxiv.2605.07074"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07074","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07074","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.2605.07074","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100462287","display_name":"Zhiyuan Wang","orcid":"https://orcid.org/0000-0002-2852-0564"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang, Zhiyuan","raw_affiliation_strings":["Hefei University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University of Technology","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101845616","display_name":"Yanxiang Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen, Yanxiang","raw_affiliation_strings":["Key Laboratory of Knowledge Engineering with Big Data"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Knowledge Engineering with Big Data","institution_ids":["https://openalex.org/I4210096250"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhao, Pengcheng","orcid":null},"institutions":[{"id":"https://openalex.org/I206777745","display_name":"Nanjing Audit University","ror":"https://ror.org/04zj2bd87","country_code":"CN","type":"education","lineage":["https://openalex.org/I206777745"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao, Pengcheng","raw_affiliation_strings":["School of Computer Science, Nanjing Audit University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Nanjing Audit University","institution_ids":["https://openalex.org/I206777745"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135860277","display_name":"Yunfeng Diao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Diao, Yunfeng","raw_affiliation_strings":["Key Laboratory of Knowledge Engineering with Big Data"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Knowledge Engineering with Big Data","institution_ids":["https://openalex.org/I4210096250"]}]},{"author_position":"last","author":{"id":null,"display_name":"Liao, Xin","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liao, Xin","raw_affiliation_strings":["College of Cyber Science and Technology, Hunan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Cyber Science and Technology, Hunan University","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"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":0.5006999969482422,"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":0.5006999969482422,"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":0.17430000007152557,"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":0.164900004863739,"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":0.5702000260353088},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5286999940872192},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.5235000252723694},{"id":"https://openalex.org/keywords/semantic-gap","display_name":"Semantic gap","score":0.5113000273704529},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4812000095844269},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47049999237060547},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4702000021934509},{"id":"https://openalex.org/keywords/quantum-entanglement","display_name":"Quantum entanglement","score":0.41830000281333923},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.4083000123500824}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6814000010490417},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5702000260353088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5437999963760376},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5286999940872192},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.5235000252723694},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.5113000273704529},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4812000095844269},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42800000309944153},{"id":"https://openalex.org/C121040770","wikidata":"https://www.wikidata.org/wiki/Q215675","display_name":"Quantum entanglement","level":3,"score":0.41830000281333923},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37229999899864197},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C50637493","wikidata":"https://www.wikidata.org/wiki/Q1136781","display_name":"Morphing","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2806999981403351},{"id":"https://openalex.org/C2780695315","wikidata":"https://www.wikidata.org/wiki/Q3799040","display_name":"Unobservable","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C198942812","wikidata":"https://www.wikidata.org/wiki/Q496618","display_name":"Semantic property","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07074","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07074","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.2605.07074","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07074","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","score":0.5351184606552124,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Detecting":[0],"AI-generated":[1],"images":[2],"across":[3],"unseen":[4,160],"architectures":[5,161],"remains":[6],"challenging,":[7],"as":[8,36,81],"existing":[9],"models":[10],"often":[11],"overfit":[12],"to":[13,29,97,109,127,139,172],"generator-specific":[14,50,66,119,150],"fingerprints":[15,51,67,151],"and":[16,52,79,93,121,135,152],"semantic":[17,53,122,129,153],"content":[18],"rather":[19],"than":[20],"learning":[21],"universal":[22,42,116,146],"forgery":[23,43,117,147],"traces.":[24],"We":[25],"attribute":[26],"this":[27,61,85],"failure":[28],"feature":[30,133],"entanglement:":[31],"detectors":[32],"learn":[33],"these":[34,100],"factors":[35],"a":[37],"single":[38],"entangled":[39],"representation,":[40],"where":[41],"traces":[44,148],"are":[45],"inextricably":[46],"confounded":[47],"with":[48],"both":[49],"content.":[54],"Crucially,":[55],"our":[56],"spectral":[57],"analysis":[58],"reveals":[59],"that":[60,167],"entanglement":[62],"is":[63,170],"avoidable:":[64],"distinct":[65],"(e.g.,":[68,162],"GAN":[69],"stripes":[70],"vs.":[71],"Diffusion":[72,164],"Model":[73],"spots)":[74],"occupy":[75],"disjoint":[76],"frequency":[77],"subspaces":[78],"coexist":[80],"independent":[82],"superpositions.":[83],"Leveraging":[84],"physical":[86],"orthogonality,":[87],"we":[88],"propose":[89],"the":[90],"Orthogonal":[91,107],"Decomposition":[92,108],"Purification":[94,126],"Network":[95],"(ODP-Net)":[96],"structurally":[98],"disentangle":[99],"factors.":[101],"Specifically,":[102],"ODP-Net":[103,155],"employs":[104],"(1)":[105],"Instance-aware":[106],"project":[110],"features":[111],"into":[112],"mutually":[113],"exclusive":[114],"subspaces:":[115],"traces,":[118],"fingerprints,":[120],"content;":[123],"(2)":[124],"Perturbation-based":[125],"enforce":[128],"invariance":[130],"via":[131],"cross-sample":[132],"injection;":[134],"(3)":[136],"Manifold":[137],"Alignment":[138],"bridge":[140],"domain":[141],"gaps.":[142],"By":[143],"explicitly":[144],"decoupling":[145],"from":[149],"content,":[154],"achieves":[156],"state-of-the-art":[157],"performance":[158],"on":[159],"Stable":[163],"3),":[165],"validating":[166],"structural":[168],"disentanglement":[169],"key":[171],"generalization.":[173]},"counts_by_year":[],"updated_date":"2026-07-21T08:15:58.654021","created_date":"2026-05-12T00:00:00"}