{"id":"https://openalex.org/W3129501265","doi":"https://doi.org/10.1109/globecom42002.2020.9348184","title":"A Practical Black-Box Attack Against Autonomous Speech Recognition Model","display_name":"A Practical Black-Box Attack Against Autonomous Speech Recognition Model","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3129501265","doi":"https://doi.org/10.1109/globecom42002.2020.9348184","mag":"3129501265"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9348184","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9348184","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102621887","display_name":"Wenshu Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenshu Fan","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100325334","display_name":"Hongwei Li","orcid":"https://orcid.org/0000-0002-1961-7946"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Li","raw_affiliation_strings":["Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066542952","display_name":"Wenbo Jiang","orcid":"https://orcid.org/0000-0002-4592-8094"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbo Jiang","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046950426","display_name":"Guowen Xu","orcid":"https://orcid.org/0000-0002-9764-9345"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guowen Xu","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070447777","display_name":"Rongxing Lu","orcid":"https://orcid.org/0000-0001-5720-0941"},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Rongxing Lu","raw_affiliation_strings":["Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada","institution_ids":["https://openalex.org/I106938459"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8507417440414429},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.7021759748458862},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.6912391185760498},{"id":"https://openalex.org/keywords/evasion","display_name":"Evasion (ethics)","score":0.6217707991600037},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4814264476299286},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4795660376548767},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4468756318092346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42578428983688354},{"id":"https://openalex.org/keywords/threat-model","display_name":"Threat model","score":0.42176392674446106},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36141517758369446},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.22638121247291565}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8507417440414429},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.7021759748458862},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.6912391185760498},{"id":"https://openalex.org/C2781251061","wikidata":"https://www.wikidata.org/wiki/Q5416089","display_name":"Evasion (ethics)","level":3,"score":0.6217707991600037},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4814264476299286},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4795660376548767},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4468756318092346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42578428983688354},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.42176392674446106},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36141517758369446},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.22638121247291565},{"id":"https://openalex.org/C203014093","wikidata":"https://www.wikidata.org/wiki/Q101929","display_name":"Immunology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C8891405","wikidata":"https://www.wikidata.org/wiki/Q1059","display_name":"Immune system","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom42002.2020.9348184","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9348184","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G2792189980","display_name":null,"funder_award_id":"2017YFB0802300,2017YFB0802000","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W9657784","https://openalex.org/W1595159159","https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2180612164","https://openalex.org/W2293768274","https://openalex.org/W2407023693","https://openalex.org/W2408141691","https://openalex.org/W2486441166","https://openalex.org/W2594717275","https://openalex.org/W2603766943","https://openalex.org/W2624801495","https://openalex.org/W2751902866","https://openalex.org/W2766001563","https://openalex.org/W2782403400","https://openalex.org/W2957377429","https://openalex.org/W2963207607","https://openalex.org/W2964097310","https://openalex.org/W2964153729","https://openalex.org/W2964301649","https://openalex.org/W3010058322","https://openalex.org/W3103836116","https://openalex.org/W4300824008","https://openalex.org/W6714171909","https://openalex.org/W6722479552"],"related_works":["https://openalex.org/W2373230814","https://openalex.org/W4375867731","https://openalex.org/W2169868145","https://openalex.org/W2127991899","https://openalex.org/W4377865163","https://openalex.org/W3184396788","https://openalex.org/W3089060762","https://openalex.org/W3109877713","https://openalex.org/W4311595005","https://openalex.org/W4295136046"],"abstract_inverted_index":{"With":[0],"the":[1,35,68,93,131,137,158,167,184,187],"wild":[2],"applications":[3,38],"of":[4,29,37,127,186],"machine":[5],"learning":[6],"(ML)":[7],"technology,":[8],"automatic":[9],"speech":[10],"recognition":[11],"(ASR)":[12],"has":[13],"made":[14],"great":[15,22],"progress":[16],"in":[17,51,79,92,100],"recent":[18],"years.":[19],"Despite":[20],"its":[21],"potential,":[23],"there":[24,54],"are":[25,164],"various":[26],"evasion":[27,88],"attacks":[28,50,62,175],"ML-based":[30],"ASR,":[31,52],"which":[32,96,114],"could":[33],"affect":[34],"security":[36],"built":[39],"upon":[40],"ASR.":[41],"Up":[42],"to":[43,60,71,121,129,146],"now,":[44],"most":[45],"studies":[46],"focus":[47],"on":[48,136],"white-box":[49],"and":[53,150,166],"is":[55,97],"almost":[56],"no":[57],"attention":[58],"paid":[59],"black-box":[61,152],"where":[63],"attackers":[64],"can":[65],"only":[66],"query":[67,130],"target":[69,132],"model":[70,109],"get":[72],"output":[73],"labels":[74],"rather":[75],"than":[76],"probability":[77],"vectors":[78],"audio":[80],"domain.":[81],"In":[82],"this":[83],"paper,":[84],"we":[85,104,117,140],"propose":[86],"an":[87],"attack":[89,153],"against":[90,154],"ASR":[91,155],"above-mentioned":[94],"situation,":[95],"more":[98],"feasible":[99],"realistic":[101],"scenarios.":[102],"Specifically,":[103],"first":[105],"train":[106,122],"a":[107,124],"substitute":[108,138],"by":[110],"using":[111],"data":[112,189],"augmentation,":[113],"ensures":[115],"that":[116,170],"have":[118],"enough":[119],"samples":[120],"with":[123,176],"small":[125],"number":[126],"times":[128],"model.":[133],"Then,":[134],"based":[135],"model,":[139],"apply":[141],"Differential":[142],"Evolution":[143],"(DE)":[144],"algorithm":[145],"craft":[147],"adversarial":[148],"examples":[149],"implement":[151],"models":[156],"from":[157],"Speech":[159],"Commands":[160],"dataset.":[161],"Extensive":[162],"experiments":[163],"conducted,":[165],"results":[168],"illustrate":[169],"our":[171],"approach":[172],"achieves":[173],"untargeted":[174],"over":[177],"70%":[178],"success":[179],"rate":[180],"while":[181],"still":[182],"maintaining":[183],"authenticity":[185],"original":[188],"well.":[190]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}