{"id":"https://openalex.org/W3187104202","doi":"https://doi.org/10.24963/ijcai.2021/109","title":"Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting","display_name":"Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting","publication_year":2021,"publication_date":"2021-08-01","ids":{"openalex":"https://openalex.org/W3187104202","doi":"https://doi.org/10.24963/ijcai.2021/109","mag":"3187104202"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/109","pdf_url":"https://www.ijcai.org/proceedings/2021/0109.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2021/0109.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062768023","display_name":"Ang Li","orcid":"https://orcid.org/0000-0002-6269-6432"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ang Li","raw_affiliation_strings":["The University of Melbourne"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083239184","display_name":"Qiuhong Ke","orcid":"https://orcid.org/0000-0001-9998-3614"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Qiuhong Ke","raw_affiliation_strings":["The University of Melbourne"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078711649","display_name":"Xingjun Ma","orcid":"https://orcid.org/0000-0003-2099-4973"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xingjun Ma","raw_affiliation_strings":["Deakin University"],"affiliations":[{"raw_affiliation_string":"Deakin University","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026598555","display_name":"Haiqin Weng","orcid":"https://orcid.org/0000-0002-3005-761X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haiqin Weng","raw_affiliation_strings":["Ant Group"],"affiliations":[{"raw_affiliation_string":"Ant Group","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027354860","display_name":"Zhiyuan Zong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiyuan Zong","raw_affiliation_strings":["Ant Group"],"affiliations":[{"raw_affiliation_string":"Ant Group","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058354365","display_name":"Feng Xue","orcid":"https://orcid.org/0000-0002-4101-3401"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng Xue","raw_affiliation_strings":["Ant Group"],"affiliations":[{"raw_affiliation_string":"Ant Group","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100422091","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0002-8104-5432"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.782,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":13,"citation_normalized_percentile":{"value":0.616717,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":90},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9986,"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":0.9986,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9985,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9943,"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/inpainting","display_name":"Inpainting","score":0.97423124},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6326322},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.52474713},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5169553}],"concepts":[{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.97423124},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8052888},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.756777},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6514731},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6326322},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.61656183},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.553872},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5416513},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5354265},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5299286},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.52474713},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5169553},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.45094562},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.43321043},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18735215},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15402985},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/109","pdf_url":"https://www.ijcai.org/proceedings/2021/0109.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2106.01532","pdf_url":"https://arxiv.org/pdf/2106.01532","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/109","pdf_url":"https://www.ijcai.org/proceedings/2021/0109.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":24,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W2014400824","https://openalex.org/W2112210925","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2732026016","https://openalex.org/W2737955903","https://openalex.org/W2738588019","https://openalex.org/W2802701183","https://openalex.org/W2808402081","https://openalex.org/W2884561390","https://openalex.org/W2895129173","https://openalex.org/W2907295878","https://openalex.org/W2916798096","https://openalex.org/W2948407537","https://openalex.org/W2963420272","https://openalex.org/W2964146055","https://openalex.org/W2966091529","https://openalex.org/W2982763192","https://openalex.org/W2994855290","https://openalex.org/W3034196597","https://openalex.org/W3035512475","https://openalex.org/W3043547428","https://openalex.org/W4320013936"],"related_works":["https://openalex.org/W4286894504","https://openalex.org/W3133417989","https://openalex.org/W2995115364","https://openalex.org/W2370766994","https://openalex.org/W2262668847","https://openalex.org/W2093556634","https://openalex.org/W2059339452","https://openalex.org/W2020564930","https://openalex.org/W166251047","https://openalex.org/W1574999717"],"abstract_inverted_index":{"Deep":[0],"image":[1,12,28,41,117],"inpainting":[2,31,51,88,170],"aims":[3,54],"to":[4,55,101,119,131,167],"restore":[5],"damaged":[6],"or":[7],"missing":[8],"regions":[9,59],"in":[10,60,113,138],"an":[11,61],"with":[13],"realistic":[14],"contents.":[15],"While":[16],"having":[17],"a":[18,96,103,126,161],"wide":[19],"range":[20],"of":[21,37,74,182],"applications":[22],"such":[23,47],"as":[24],"object":[25],"removal":[26],"and":[27,142,164],"recovery,":[29],"deep":[30,50,75,87,169],"techniques":[32],"also":[33,177],"have":[34],"the":[35,57,68,78,109,134,140,180],"risk":[36],"being":[38],"manipulated":[39],"for":[40],"forgery.":[42],"A":[43],"promising":[44],"countermeasure":[45],"against":[46],"forgeries":[48],"is":[49],"detection,":[52],"which":[53,107],"locate":[56],"inpainted":[58,116],"image.":[62],"In":[63],"this":[64,91],"paper,":[65],"we":[66,93],"make":[67],"first":[69,94],"attempt":[70],"towards":[71],"universal":[72,104,121,173],"detection":[73,79,158,184],"inpainting,":[76],"where":[77],"network":[80],"can":[81,176],"generalize":[82,165],"well":[83,166],"when":[84],"detecting":[85],"different":[86],"methods.":[89,185],"To":[90],"end,":[92],"propose":[95],"novel":[97],"data":[98],"generation":[99],"approach":[100,155],"generate":[102],"training":[105,174],"dataset,":[106],"imitates":[108],"noise":[110,144],"discrepancies":[111],"exist":[112],"real":[114],"versus":[115],"contents":[118],"train":[120],"detectors.":[122],"We":[123,146],"then":[124],"design":[125],"Noise-Image":[127],"Cross-fusion":[128],"Network":[129],"(NIX-Net)":[130],"effectively":[132],"exploit":[133],"discriminative":[135],"information":[136],"contained":[137],"both":[139],"images":[141],"their":[143],"patterns.":[145],"empirically":[147],"show,":[148],"on":[149],"multiple":[150],"benchmark":[151],"datasets,":[152],"that":[153],"our":[154],"outperforms":[156],"existing":[157,183],"methods":[159],"by":[160],"large":[162],"margin":[163],"unseen":[168],"techniques.":[171],"Our":[172],"dataset":[175],"significantly":[178],"boost":[179],"generalizability":[181]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3187104202","counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3}],"updated_date":"2025-01-17T23:07:54.352388","created_date":"2021-08-16"}