{"id":"https://openalex.org/W4383474014","doi":"https://doi.org/10.48550/arxiv.2307.02457","title":"DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models","display_name":"DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models","publication_year":2023,"publication_date":"2023-07-05","ids":{"openalex":"https://openalex.org/W4383474014","doi":"https://doi.org/10.48550/arxiv.2307.02457"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2307.02457","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.02457","pdf_url":"https://arxiv.org/pdf/2307.02457","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2307.02457","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065367221","display_name":"Liangbin Xie","orcid":"https://orcid.org/0009-0009-1931-517X","observed_orcids":["https://orcid.org/0009-0009-1931-517X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Liangbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101538534","display_name":"Xintao Wang","orcid":"https://orcid.org/0000-0001-6585-8604","observed_orcids":["https://orcid.org/0000-0001-6585-8604"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xintao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100776496","display_name":"Xiangyu Chen","orcid":"https://orcid.org/0000-0001-5858-5679","observed_orcids":["https://orcid.org/0000-0001-5858-5679"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100785866","display_name":"Gen Li","orcid":"https://orcid.org/0000-0001-6636-1106","observed_orcids":["https://orcid.org/0000-0001-6636-1106"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Gen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102004349","display_name":"Ying Shan","orcid":"https://orcid.org/0000-0001-7673-8325","observed_orcids":["https://orcid.org/0000-0001-7673-8325"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shan, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037979193","display_name":"Jiantao Zhou","orcid":"https://orcid.org/0000-0002-6015-2618","observed_orcids":["https://orcid.org/0000-0002-6015-2618"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Jiantao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100319450","display_name":"Chao Dong","orcid":"https://orcid.org/0000-0001-9965-0852","observed_orcids":["https://orcid.org/0000-0001-9965-0852"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":17,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9911999702453613,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9911999702453613,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.005400000140070915,"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":0.0010999999940395355,"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/image-super-resolution","display_name":"image super-resolution","score":0.9750000238418579},{"id":"https://openalex.org/keywords/artifact-detection","display_name":"artifact detection","score":0.7860000133514404},{"id":"https://openalex.org/keywords/artifact-removal","display_name":"artifact removal","score":0.9340000152587891},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"fine-tuning","score":0.6629999876022339}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7813873291015625},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.737909197807312},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6714471578598022},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.64232337474823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5878446698188782},{"id":"https://openalex.org/C141239990","wikidata":"https://www.wikidata.org/wiki/Q957423","display_name":"Superresolution","level":3,"score":0.5215139985084534},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5155532360076904},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.50450599193573},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4735231399536133},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4532642066478729},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.42152905464172363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4022792875766754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3928769826889038},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3478061854839325},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32145774364471436},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2307.02457","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.02457","pdf_url":"https://arxiv.org/pdf/2307.02457","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2307.02457","doi":"https://doi.org/10.48550/arxiv.2307.02457","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2307.02457","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2307.02457","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.02457","pdf_url":"https://arxiv.org/pdf/2307.02457","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4223483730","https://openalex.org/W4392971862","https://openalex.org/W4411232113","https://openalex.org/W3035550118","https://openalex.org/W4402754219","https://openalex.org/W4389261199","https://openalex.org/W4413411874","https://openalex.org/W4390872407","https://openalex.org/W4402770985","https://openalex.org/W7130696741"],"abstract_inverted_index":{"Image":[0],"super-resolution":[1],"(SR)":[2],"with":[3,40,159,168,179],"generative":[4],"adversarial":[5],"networks":[6],"(GAN)":[7],"has":[8],"achieved":[9],"great":[10],"success":[11],"in":[12,32,45,61,91,113,173,199],"restoring":[13],"realistic":[14],"details.":[15],"However,":[16],"it":[17],"is":[18],"notorious":[19],"that":[20,66,164],"GAN-based":[21,156],"SR":[22,111,157,194],"models":[23,158,195],"will":[24,204],"inevitably":[25],"produce":[26],"unpleasant":[27],"and":[28,84,107,128,131,141,189],"undesirable":[29],"artifacts,":[30],"especially":[31],"practical":[33],"scenarios.":[34,201],"Previous":[35],"works":[36],"typically":[37],"suppress":[38],"artifacts":[39,74,89,172,186],"an":[41],"extra":[42],"loss":[43],"penalty":[44],"the":[46,82,87,133,138,146,191],"training":[47],"phase.":[48],"They":[49],"only":[50],"work":[51],"for":[52],"in-distribution":[53],"artifact":[54,147],"types":[55,170],"generated":[56],"during":[57,75],"training.":[58],"When":[59],"applied":[60,198],"real-world":[62,200],"scenarios,":[63],"we":[64,80,116,149,182],"observe":[65],"those":[67,110],"improved":[68],"methods":[69],"still":[70],"generate":[71],"obviously":[72],"annoying":[73],"inference.":[76],"In":[77],"this":[78],"paper,":[79],"analyze":[81],"cause":[83],"characteristics":[85],"of":[86,171,193],"GAN":[88],"produced":[90],"unseen":[92,175],"test":[93],"data":[94],"without":[95],"ground-truths.":[96],"We":[97],"then":[98,108],"develop":[99,150],"a":[100,120,151,160],"novel":[101],"method,":[102],"namely,":[103],"DeSRA,":[104,181],"to":[105,118,154,196],"Detect":[106],"Delete":[109],"Artifacts":[112],"practice.":[114],"Specifically,":[115],"propose":[117],"measure":[119],"relative":[121],"local":[122],"variance":[123],"distance":[124,140],"from":[125,187],"MSE-SR":[126],"results":[127],"GAN-SR":[129],"results,":[130],"locate":[132],"problematic":[134],"areas":[135],"based":[136],"on":[137],"above":[139],"semantic-aware":[142],"thresholds.":[143],"After":[144],"detecting":[145],"regions,":[148],"finetune":[152],"procedure":[153],"improve":[155,190],"few":[161],"samples,":[162],"so":[163],"they":[165],"can":[166,183],"deal":[167],"similar":[169],"more":[174],"real":[176],"data.":[177],"Equipped":[178],"our":[180],"successfully":[184],"eliminate":[185],"inference":[188],"ability":[192],"be":[197,205],"The":[202],"code":[203],"available":[206],"at":[207],"https://github.com/TencentARC/DeSRA.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":6}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2023-07-07T00:00:00"}