{"id":"https://openalex.org/W2944898591","doi":"https://doi.org/10.1145/3316781.3317861","title":"MASKER","display_name":"MASKER","publication_year":2019,"publication_date":"2019-05-23","ids":{"openalex":"https://openalex.org/W2944898591","doi":"https://doi.org/10.1145/3316781.3317861","mag":"2944898591"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3316781.3317861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103085687","display_name":"Fuxun Yu","orcid":"https://orcid.org/0000-0002-4880-6658"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"funder","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fuxun Yu","raw_affiliation_strings":["George Mason University, Fairfax, Virginia"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, Virginia","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030027584","display_name":"Zirui Xu","orcid":"https://orcid.org/0000-0002-3556-9358"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"funder","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zirui Xu","raw_affiliation_strings":["George Mason University, Fairfax, Virginia"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, Virginia","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767204","display_name":"Chenchen Liu","orcid":"https://orcid.org/0000-0001-9522-8312"},"institutions":[{"id":"https://openalex.org/I16944753","display_name":"Clarkson University","ror":"https://ror.org/03rwgpn18","country_code":"US","type":"funder","lineage":["https://openalex.org/I16944753"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenchen Liu","raw_affiliation_strings":["Clarkson University, Potsdam, New York"],"affiliations":[{"raw_affiliation_string":"Clarkson University, Potsdam, New York","institution_ids":["https://openalex.org/I16944753"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100441957","display_name":"Xiang Chen","orcid":"https://orcid.org/0000-0003-2790-976X"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"funder","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Chen","raw_affiliation_strings":["George Mason University, Fairfax, Virginia"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, Virginia","institution_ids":["https://openalex.org/I162714631"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.198,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":6,"citation_normalized_percentile":{"value":0.496117,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":81,"max":82},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.986,"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":0.986,"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":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45896104}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3316781.3317861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":15,"referenced_works":["https://openalex.org/W1920908428","https://openalex.org/W1922655562","https://openalex.org/W1945616565","https://openalex.org/W2028526753","https://openalex.org/W2057731362","https://openalex.org/W2108825861","https://openalex.org/W2150387003","https://openalex.org/W2255539412","https://openalex.org/W2402144811","https://openalex.org/W2784500886","https://openalex.org/W2963211739","https://openalex.org/W2964301649","https://openalex.org/W3139612913","https://openalex.org/W3151748292","https://openalex.org/W4232992964"],"related_works":["https://openalex.org/W4391913857","https://openalex.org/W2748952813","https://openalex.org/W2530322880","https://openalex.org/W2478288626","https://openalex.org/W2390279801","https://openalex.org/W2382290278","https://openalex.org/W2376932109","https://openalex.org/W2358668433","https://openalex.org/W2350741829","https://openalex.org/W2001405890"],"abstract_inverted_index":{"Benefited":[0],"from":[1,65],"recent":[2],"artificial":[3],"intelligence":[4],"evolution,":[5],"Automatic":[6],"Speech":[7],"Recognition":[8],"(ASR)":[9],"technology":[10],"has":[11,142],"achieved":[12],"enormous":[13],"performance":[14],"improvement":[15],"and":[16,95,134,174],"wider":[17],"application.":[18],"Unfortunately,":[19],"ASR":[20,29,66,71,116,165],"is":[21,30,76,102,126],"also":[22],"heavily":[23],"leveraged":[24],"by":[25],"speech":[26,39,63,87,100],"eavesdropping,":[27],"where":[28],"used":[31],"to":[32,59,78,104,180],"translate":[33],"large":[34],"volume":[35],"of":[36,162],"intercepted":[37],"vocal":[38],"into":[40,84],"text":[41],"content,":[42],"causing":[43],"considerable":[44],"information":[45],"leakage.":[46],"In":[47],"this":[48],"work,":[49],"we":[50],"propose":[51],"MASKER":[52,75,125,141,153],"--":[53],"a":[54],"mobile":[55,62,90,130,147],"security":[56,156],"enhancement":[57,157],"solution":[58],"protect":[60],"the":[61,85,89,99,109,115,181],"data":[64,101,107],"in":[67],"eavesdropping.":[68],"By":[69],"identifying":[70],"models'":[72],"ubiquitous":[73],"vulnerability,":[74],"designed":[77],"generate":[79],"human":[80],"imperceptible":[81],"adversarial":[82,110],"noises":[83,111,138],"real-time":[86],"on":[88],"device":[91],"(e.g.":[92],"phone":[93],"call":[94],"voice":[96],"message).":[97],"Even":[98],"exposed":[103],"eavesdropping":[105],"during":[106],"transmission,":[108],"can":[112,154],"effectively":[113],"perturb":[114],"process":[117],"with":[118,158],"significant":[119],"Word":[120],"Error":[121],"Rate":[122],"(WER).":[123],"Meanwhile,":[124],"further":[127],"optimized":[128],"for":[129,136,146,164,170],"user":[131,171],"perception":[132,172],"quality":[133,173],"enhanced":[135],"environmental":[137],"adaptation.":[139],"Moreover,":[140],"outstanding":[143],"computation":[144],"efficiency":[145],"system":[148],"integration.":[149],"Experiments":[150],"show":[151],"that,":[152],"achieve":[155],"an":[159],"average":[160],"WER":[161],"84.55%":[163],"perturbation,":[166],"32%":[167],"noise":[168],"reduction":[169],"16\u00d7":[175],"faster":[176],"processing":[177],"speed":[178],"compared":[179],"state-of-the-art":[182],"method.":[183]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2944898591","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2025-02-25T11:30:48.088746","created_date":"2019-05-29"}