{"id":"https://openalex.org/W4416513164","doi":"https://doi.org/10.1109/access.2025.3635633","title":"Inference-Time Noise Addition for Improving Adversarial Robustness of Audio Deepfake Detection System","display_name":"Inference-Time Noise Addition for Improving Adversarial Robustness of Audio Deepfake Detection System","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416513164","doi":"https://doi.org/10.1109/access.2025.3635633"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3635633","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3635633","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3635633","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115060179","display_name":"Inho Kim","orcid":"https://orcid.org/0009-0001-0876-1803"},"institutions":[{"id":"https://openalex.org/I141371507","display_name":"Soongsil University","ror":"https://ror.org/017xnm587","country_code":"KR","type":"education","lineage":["https://openalex.org/I141371507"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Inho Kim","raw_affiliation_strings":["Soongsil University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0001-0876-1803","affiliations":[{"raw_affiliation_string":"Soongsil University, Seoul, South Korea","institution_ids":["https://openalex.org/I141371507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064475040","display_name":"Thien-Phuc Doan","orcid":"https://orcid.org/0000-0001-7988-5953"},"institutions":[{"id":"https://openalex.org/I141371507","display_name":"Soongsil University","ror":"https://ror.org/017xnm587","country_code":"KR","type":"education","lineage":["https://openalex.org/I141371507"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Thien-Phuc Doan","raw_affiliation_strings":["Soongsil University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-7988-5953","affiliations":[{"raw_affiliation_string":"Soongsil University, Seoul, South Korea","institution_ids":["https://openalex.org/I141371507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102751625","display_name":"Sanghyun Hong","orcid":"https://orcid.org/0000-0003-4154-7611"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanghyun Hong","raw_affiliation_strings":["Oregon State University, Corvallis, OR, USA"],"raw_orcid":"https://orcid.org/0000-0003-4154-7611","affiliations":[{"raw_affiliation_string":"Oregon State University, Corvallis, OR, USA","institution_ids":["https://openalex.org/I131249849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031598830","display_name":"Souhwan Jung","orcid":"https://orcid.org/0000-0003-2676-3412"},"institutions":[{"id":"https://openalex.org/I141371507","display_name":"Soongsil University","ror":"https://ror.org/017xnm587","country_code":"KR","type":"education","lineage":["https://openalex.org/I141371507"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Souhwan Jung","raw_affiliation_strings":["Soongsil University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2676-3412","affiliations":[{"raw_affiliation_string":"Soongsil University, Seoul, South Korea","institution_ids":["https://openalex.org/I141371507"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.9127,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.93956725,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"200669","last_page":"200682"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.3481999933719635,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.3481999933719635,"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":0.24079999327659607,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.07919999957084656,"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/adversarial-system","display_name":"Adversarial system","score":0.7727000117301941},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6098999977111816},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5805000066757202},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5299000144004822},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5063999891281128},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4885999858379364},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.45089998841285706},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4481000006198883}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8949999809265137},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7727000117301941},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6098999977111816},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5805000066757202},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5521000027656555},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5299000144004822},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5063999891281128},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49480000138282776},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.45089998841285706},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.44530001282691956},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4196000099182129},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.28189998865127563},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27730000019073486},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2639000117778778}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3635633","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3635633","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9b160be538ca4d1bb4ba2e8033319070","is_oa":true,"landing_page_url":"https://doaj.org/article/9b160be538ca4d1bb4ba2e8033319070","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 200669-200682 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3635633","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3635633","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6167614159","display_name":null,"funder_award_id":"RS-2023-00263037","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"}],"funders":[{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0,80],"artificial":[1],"intelligence":[2],"technology":[3],"advances,":[4],"the":[5,55,62,158,168,173,218,233,242,261,266,271,286],"generalization":[6],"performance":[7,160,195],"of":[8,48,57,64,119,217,220,263,275,288],"audio":[9,58,170,239],"deepfake":[10,59,67,83,238],"detection":[11,68,84,276],"systems":[12,20,85,277],"has":[13],"improved":[14],"rapidly":[15],"and":[16,73,92,111,150,201,222,250,270,280,297],"significantly.":[17],"However,":[18,124],"these":[19],"still":[21],"face":[22],"substantial":[23],"challenges":[24],"when":[25],"confronted":[26],"with":[27,208,293],"adversarial":[28,122,152],"attacks.":[29,42],"To":[30],"address":[31],"this":[32,140],"issue,":[33],"many":[34],"studies":[35,49],"have":[36,50,126],"proposed":[37],"defense":[38,109,154,210,300],"methods":[39,69,110],"against":[40],"such":[41,128],"Nevertheless,":[43],"only":[44],"a":[45,81,116,214],"limited":[46],"number":[47],"focused":[51],"on":[52,76,232],"defenses":[53],"in":[54,247],"context":[56],"detection.":[60],"With":[61],"development":[63],"deep":[65,77],"learning,":[66],"are":[70,93,104],"increasingly":[71],"designed":[72],"deployed":[74],"based":[75],"learning":[78],"models.":[79],"result,":[82],"can":[86,112,205],"be":[87,113,206],"applied":[88,114],"across":[89],"various":[90,254],"fields":[91],"not":[94,184],"restricted":[95],"to":[96,106,115,167],"specific":[97],"data":[98],"types":[99],"or":[100,134,188],"domains.":[101],"Such":[102],"approaches":[103],"referred":[105],"as":[107,129,181,197,199],"\u2018domain-independent\u2019":[108],"wide":[117],"range":[118],"tasks,":[120],"including":[121],"training.":[123],"they":[125],"limitations,":[127],"requiring":[130],"significant":[131],"computing":[132],"resources":[133],"potentially":[135],"degrading":[136],"original":[137,159],"performance.":[138],"In":[139],"paper,":[141],"we":[142],"propose":[143],"Inference-Time":[144],"Noise":[145],"Addition":[146],"(ITNA),":[147],"an":[148],"effective":[149],"efficient":[151],"attack":[153,272],"method":[155],"that":[156,204],"minimizes":[157,194],"degradation.":[161],"ITNA":[162,193,221,289],"adds":[163],"Gaussian":[164],"noise":[165,295],"once":[166],"input":[169],"sample":[171],"during":[172],"inference":[174],"phase.":[175],"It":[176],"is":[177],"highly":[178],"resource":[179],"efficient,":[180],"it":[182,224],"does":[183],"require":[185],"additional":[186],"training":[187],"separate":[189],"AI":[190],"components.":[191],"Furthermore,":[192],"degradation":[196],"much":[198],"possible":[200],"provides":[202],"scalability":[203],"used":[207],"other":[209],"methods.":[211],"We":[212,283],"provide":[213],"theoretical":[215],"explanation":[216],"effectiveness":[219,262,287],"validate":[223],"through":[225,290],"experimental":[226],"results.":[227],"Our":[228],"experiments":[229,258,292],"were":[230],"conducted":[231],"ASVspoof2021":[234],"DF":[235],"evaluation":[236],"(official":[237],"dataset":[240],"from":[241],"ASVspoof":[243],"community),":[244],"In-The-Wild":[245],"(collected":[246],"real":[248],"environments)":[249],"DSD-Corpus":[251],"datasets":[252],"(includes":[253],"synthesizer":[255],"samples).":[256],"And":[257],"results":[259],"demonstrate":[260,285],"ITNA,":[264],"reducing":[265],"average":[267],"misclassification":[268],"rate":[269,274],"success":[273],"by":[278],"4.48%":[279],"31.90%,":[281],"respectively.":[282],"also":[284],"comparative":[291],"alternative":[294],"type":[296],"existing":[298],"noise-based":[299],"method.":[301]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-11-23T00:00:00"}