{"id":"https://openalex.org/W4403791955","doi":"https://doi.org/10.1145/3664647.3689002","title":"Optimizing AIGC Image Detection: Strategies in Data Augmentation and Model Architecture","display_name":"Optimizing AIGC Image Detection: Strategies in Data Augmentation and Model Architecture","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791955","doi":"https://doi.org/10.1145/3664647.3689002"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3689002","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3689002","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 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3664647.3689002","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112281344","display_name":"H. J. Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huihui Fu","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5112281344"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.7412,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72917074,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"11472","last_page":"11474"},"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":0.9984999895095825,"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":0.9984999895095825,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9965999722480774,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9887999892234802,"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/computer-science","display_name":"Computer science","score":0.713994562625885},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6251891851425171},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5507951974868774},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4907039403915405},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4771803617477417},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07138076424598694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.713994562625885},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6251891851425171},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5507951974868774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4907039403915405},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4771803617477417},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07138076424598694},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3689002","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3689002","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 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3664647.3689002","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3689002","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 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W3034577585","https://openalex.org/W4386590429","https://openalex.org/W4391019749","https://openalex.org/W4402716164"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"With":[0],"the":[1,14,29,55,59,106,138,147,154,158,165,174,180,187,195,200],"development":[2],"of":[3,32,38,58,68,108,168,179],"AIGC,":[4],"images":[5,67,76,86,93,185],"generated":[6,184],"by":[7,126],"AI":[8],"are":[9,42,64,98],"difficult":[10],"to":[11,53,120,132,141],"identify":[12,142],"with":[13],"human":[15],"eye.":[16],"This":[17],"article":[18],"proposes":[19],"a":[20,25,36,130],"simple":[21,26],"training":[22,30],"architecture":[23],"and":[24,50,89,92,101,177],"model.":[27,60,181],"In":[28],"process":[31],"deep":[33],"forgery":[34,148],"detection,":[35],"variety":[37],"data":[39],"enhancement":[40],"techniques":[41],"used,":[43],"including":[44],"horizontal":[45],"flipping,":[46],"adding":[47],"Gaussian":[48],"noise":[49],"random":[51,81],"cropping":[52,72,82],"enhance":[54],"generalization":[56],"ability":[57,140],"Differentiated":[61],"processing":[62],"strategies":[63],"adopted":[65],"for":[66,75,85,199],"different":[69],"sizes:":[70],"center":[71],"is":[73,83,113,116,150],"used":[74,84],"smaller":[77],"than":[78,95],"224":[79,88],"pixels,":[80,91],"between":[87],"512":[90,96],"larger":[94],"pixels":[97],"first":[99,196],"scaled":[100],"then":[102],"randomly":[103],"cropped.":[104],"Furthermore,":[105],"concept":[107],"neighbor":[109],"pixel":[110],"relations":[111],"(NPR)":[112],"introduced,":[114],"which":[115],"an":[117],"effective":[118],"way":[119],"capture":[121],"local":[122],"structural":[123],"artifacts":[124],"introduced":[125],"upsampling":[127],"operations.":[128],"As":[129],"complement":[131],"image":[133],"representation,":[134],"NPR":[135,155],"significantly":[136],"improves":[137],"detector's":[139],"forged":[143,169],"images,":[144],"especially":[145],"when":[146],"technology":[149],"unknown.":[151],"By":[152],"integrating":[153],"module":[156],"into":[157],"ResNet-50":[159],"model,":[160],"it":[161],"not":[162],"only":[163],"enhances":[164],"classifier's":[166],"extraction":[167],"features,":[170],"but":[171],"also":[172],"maintains":[173],"computational":[175],"efficiency":[176],"versatility":[178],"Finally,":[182],"AIGC":[183],"in":[186],"''Malanshan":[188],"Cup\"":[189],"International":[190],"Audio-Video":[191],"Algorithm":[192],"Competition.":[193],"Determine":[194],"place":[197],"result":[198],"track.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}