{"id":"https://openalex.org/W7164831028","doi":"https://doi.org/10.1145/3805622.3810615","title":"Ivy-Fake: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection","display_name":"Ivy-Fake: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164831028","doi":"https://doi.org/10.1145/3805622.3810615"},"language":null,"primary_location":{"id":"doi:10.1145/3805622.3810615","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810615","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805622.3810615","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134152042","display_name":"Changjiang Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changjiang Jiang","raw_affiliation_strings":["Nanjing University, Nanjing, China and Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-6813-8669","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China and Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018813392","display_name":"Wenhui Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhui Dong","raw_affiliation_strings":["Nanjing University, Nanjing, China and Pi3Lab, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0004-3154-9087","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China and Pi3Lab, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100782372","display_name":"Zhonghao Zhang","orcid":"https://orcid.org/0000-0002-4354-4636"},"institutions":[{"id":"https://openalex.org/I21642278","display_name":"Ningxia University","ror":"https://ror.org/04j7b2v61","country_code":"CN","type":"education","lineage":["https://openalex.org/I21642278"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhonghao Zhang","raw_affiliation_strings":["Ningxia University, Yinchuan, China"],"raw_orcid":"https://orcid.org/0009-0001-6491-5598","affiliations":[{"raw_affiliation_string":"Ningxia University, Yinchuan, China","institution_ids":["https://openalex.org/I21642278"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051738938","display_name":"Fengchang Yu","orcid":"https://orcid.org/0000-0002-6503-4688"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengchang Yu","raw_affiliation_strings":["Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-6503-4688","affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019479187","display_name":"Wei Peng","orcid":"https://orcid.org/0000-0002-2892-5764"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Peng","raw_affiliation_strings":["Stanford University, Stanford, USA"],"raw_orcid":"https://orcid.org/0000-0002-2892-5764","affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015965681","display_name":"Xinbin Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinbin Yuan","raw_affiliation_strings":["Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0009-8715-5497","affiliations":[{"raw_affiliation_string":"Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031059333","display_name":"Yifei Bi","orcid":"https://orcid.org/0000-0001-7254-5403"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yifei Bi","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, USA"],"raw_orcid":"https://orcid.org/0009-0001-0900-6834","affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100846265","display_name":"Ming Zhao","orcid":"https://orcid.org/0000-0001-7431-1897"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Zhao","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0004-9963-4296","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138652022","display_name":"Zian Zhou","orcid":"https://orcid.org/0009-0002-7380-3608"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zian Zhou","raw_affiliation_strings":["Zhejiang University, Zhejiang, China"],"raw_orcid":"https://orcid.org/0009-0002-7380-3608","affiliations":[{"raw_affiliation_string":"Zhejiang University, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109312319","display_name":"Chong-wen Si","orcid":"https://orcid.org/0009-0002-1725-5766"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyang Si","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0002-1725-5766","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055478558","display_name":"Caifeng Shan","orcid":"https://orcid.org/0000-0002-2131-1671"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caifeng Shan","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-2131-1671","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"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":"2438","last_page":"2447"},"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":0.36169999837875366,"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":0.36169999837875366,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.15569999814033508,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.12370000034570694,"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/interpretability","display_name":"Interpretability","score":0.8737999796867371},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6798999905586243},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5625},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5131000280380249},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4941999912261963},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4307999908924103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3806000053882599},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.3765999972820282},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.35670000314712524}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8737999796867371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8181999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7002000212669373},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6798999905586243},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5625},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5131000280380249},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47290000319480896},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4307999908924103},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36419999599456787},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.35670000314712524},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.2948000133037567},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2800999879837036},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.25519999861717224},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805622.3810615","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810615","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805622.3810615","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810615","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4374959170818329}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2117539524","https://openalex.org/W2425121537","https://openalex.org/W3034577585","https://openalex.org/W3094728142","https://openalex.org/W4312933868","https://openalex.org/W4386076109","https://openalex.org/W4390872250","https://openalex.org/W4390873235","https://openalex.org/W4399048893","https://openalex.org/W4402713111","https://openalex.org/W4402754093","https://openalex.org/W4403792332","https://openalex.org/W4406160881","https://openalex.org/W4408403226","https://openalex.org/W4409346712","https://openalex.org/W4409365647","https://openalex.org/W4409368310","https://openalex.org/W4411892026","https://openalex.org/W4415536278","https://openalex.org/W4415800688","https://openalex.org/W4416035182","https://openalex.org/W7133185348","https://openalex.org/W7133239970","https://openalex.org/W7138031105","https://openalex.org/W7155091541","https://openalex.org/W7160054321"],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"development":[2],"of":[3,14,35,61,105,156],"Artificial":[4],"Intelligence":[5],"Generated":[6],"Content":[7],"(AIGC)":[8],"techniques":[9],"has":[10],"enabled":[11],"the":[12,33,57,92],"creation":[13],"high-quality":[15],"synthetic":[16],"content,":[17],"but":[18],"it":[19],"also":[20],"raises":[21],"significant":[22],"security":[23],"concerns.":[24],"Current":[25],"detection":[26,171],"methods":[27],"face":[28],"two":[29],"major":[30],"limitations:":[31],"(1)":[32],"lack":[34],"multidimensional":[36],"explainable":[37,158],"datasets":[38,46,128],"for":[39,97],"generated":[40],"images":[41],"and":[42,59,83,100,113,115,126,138,161,169],"videos.":[43],"Existing":[44],"open-source":[45],"(e.g.,":[47,69],"WildFake,":[48],"GenVideo)":[49],"rely":[50],"on":[51,151],"oversimplified":[52],"binary":[53],"annotations,":[54],"which":[55,79],"restrict":[56],"explainability":[58],"trustworthiness":[60],"trained":[62],"detectors.":[63],"(2)":[64],"Prior":[65],"MLLM-based":[66],"forgery":[67],"detectors":[68],"FakeVLM)":[70],"exhibit":[71],"insufficiently":[72],"fine-grained":[73],"interpretability":[74],"in":[75],"their":[76],"step-by-step":[77],"reasoning,":[78],"hinders":[80],"reliable":[81],"localization":[82],"explanation.":[84],"To":[85],"address":[86],"these":[87],"challenges,":[88],"we":[89,141],"introduce":[90],"Ivy-Fake,":[91],"first":[93],"large-scale":[94],"multimodal":[95],"benchmark":[96],"fake":[98,167],"image":[99,168],"video":[101,170],"detection.":[102],"It":[103],"consists":[104],"over":[106],"106K":[107],"richly":[108],"annotated":[109],"training":[110],"samples":[111],"(images":[112],"videos)":[114],"5,000":[116],"manually":[117],"verified":[118],"evaluation":[119],"examples,":[120],"sourced":[121],"from":[122],"multiple":[123,166],"generative":[124],"models":[125],"real-world":[127],"through":[129],"a":[130,144],"carefully":[131],"designed":[132],"pipeline":[133],"to":[134],"ensure":[135],"both":[136],"diversity":[137],"quality.":[139],"Furthermore,":[140],"propose":[142],"Ivy-xDetector,":[143],"multimodel":[145],"large":[146],"language":[147],"model":[148],"(MLLM)":[149],"based":[150],"reinforcement":[152],"fine-tuning":[153],"(RFT),":[154],"capable":[155],"producing":[157],"reasoning":[159],"chains":[160],"achieving":[162],"robust":[163],"performance":[164],"across":[165],"benchmarks.":[172]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-06-16T00:00:00"}
