{"id":"https://openalex.org/W3031806488","doi":"https://doi.org/10.21437/interspeech.2020-2734","title":"Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification","display_name":"Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification","publication_year":2020,"publication_date":"2020-10-25","ids":{"openalex":"https://openalex.org/W3031806488","doi":"https://doi.org/10.21437/interspeech.2020-2734","mag":"3031806488"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2020-2734","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-2734","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2005.14611","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021713263","display_name":"Sina D\u00e4ubener","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sina D\u00e4ubener","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049219646","display_name":"Lea Sch\u00f6nherr","orcid":"https://orcid.org/0000-0003-3779-7781"},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lea Sch\u00f6nherr","raw_affiliation_strings":["Ruhr University Bochum, Bochum, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr University Bochum, Bochum, Germany","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026151059","display_name":"Asja Fischer","orcid":"https://orcid.org/0000-0002-1916-7033"},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Asja Fischer","raw_affiliation_strings":["Ruhr University Bochum, Bochum, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr University Bochum, Bochum, Germany","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007017640","display_name":"Dorothea Kolossa","orcid":"https://orcid.org/0000-0003-0678-3053"},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dorothea Kolossa","raw_affiliation_strings":["Ruhr-Univ. Bochum"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr-Univ. Bochum","institution_ids":["https://openalex.org/I904495901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4661","last_page":"4665"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9951000213623047,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.758219838142395},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7574516534805298},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.622997522354126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5757289528846741},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5352789759635925},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4998500347137451},{"id":"https://openalex.org/keywords/vulnerability","display_name":"Vulnerability (computing)","score":0.49265581369400024},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46697473526000977},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4667183756828308},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4628426730632782},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43778663873672485},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36697447299957275},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.09824690222740173}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.758219838142395},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7574516534805298},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.622997522354126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5757289528846741},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5352789759635925},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4998500347137451},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.49265581369400024},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46697473526000977},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4667183756828308},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4628426730632782},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43778663873672485},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36697447299957275},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.09824690222740173},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":10,"locations":[{"id":"doi:10.21437/interspeech.2020-2734","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-2734","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2005.14611","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.14611","pdf_url":"https://arxiv.org/pdf/2005.14611","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":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3031806488","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2005.14611.pdf","pdf_url":null,"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":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:figshare.com:article/32772363","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Detecting_Adversarial_Examples_for_Speech_Recognition_via_Uncertainty_Quantification/32772363","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference contribution"},{"id":"pmh:oai:figshare.com:article/32772375","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Detecting_Adversarial_Examples_for_Speech_Recognition_via_Uncertainty_Quantification/32772375","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"doi:10.48550/arxiv.2005.14611","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2005.14611","pdf_url":null,"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":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.60882/cispa.32772363","is_oa":true,"landing_page_url":"https://doi.org/10.60882/cispa.32772363","pdf_url":null,"source":{"id":"https://openalex.org/S7407050916","display_name":"CISPA Helmholtz Center","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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"ConferenceProceeding"},{"id":"doi:10.60882/cispa.32772363.v1","is_oa":true,"landing_page_url":"https://doi.org/10.60882/cispa.32772363.v1","pdf_url":null,"source":{"id":"https://openalex.org/S7407050916","display_name":"CISPA Helmholtz Center","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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"ConferenceProceeding"},{"id":"doi:10.60882/cispa.32772375","is_oa":true,"landing_page_url":"https://doi.org/10.60882/cispa.32772375","pdf_url":null,"source":{"id":"https://openalex.org/S7407050916","display_name":"CISPA Helmholtz Center","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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"JournalArticle"},{"id":"doi:10.60882/cispa.32772375.v1","is_oa":true,"landing_page_url":"https://doi.org/10.60882/cispa.32772375.v1","pdf_url":null,"source":{"id":"https://openalex.org/S7407050916","display_name":"CISPA Helmholtz Center","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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"JournalArticle"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2005.14611","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.14611","pdf_url":"https://arxiv.org/pdf/2005.14611","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":[],"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":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3031806488.pdf","grobid_xml":"https://content.openalex.org/works/W3031806488.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1567512734","https://openalex.org/W2302053044","https://openalex.org/W2403445744","https://openalex.org/W2408141691","https://openalex.org/W2584063687","https://openalex.org/W2592505114","https://openalex.org/W2594867206","https://openalex.org/W2747874407","https://openalex.org/W2782403400","https://openalex.org/W2792388013","https://openalex.org/W2810611310","https://openalex.org/W2892852825","https://openalex.org/W2905910643","https://openalex.org/W2918967742","https://openalex.org/W2939216642","https://openalex.org/W2954469952","https://openalex.org/W2963058500","https://openalex.org/W2963215553","https://openalex.org/W2963238274","https://openalex.org/W2963564844","https://openalex.org/W2964059111","https://openalex.org/W2964253222","https://openalex.org/W2964301649","https://openalex.org/W2969908034","https://openalex.org/W2970115835","https://openalex.org/W2970859221","https://openalex.org/W2971741682","https://openalex.org/W3005939920","https://openalex.org/W3015241525"],"related_works":["https://openalex.org/W3095722698","https://openalex.org/W3196407213","https://openalex.org/W3096366160","https://openalex.org/W3015241525","https://openalex.org/W2995536569","https://openalex.org/W2963857521","https://openalex.org/W2975513267","https://openalex.org/W3202316402","https://openalex.org/W3091836876","https://openalex.org/W3005562893","https://openalex.org/W3137573682","https://openalex.org/W3083459508","https://openalex.org/W3138401595","https://openalex.org/W3017825983","https://openalex.org/W3007892172","https://openalex.org/W3114556617","https://openalex.org/W2955431898","https://openalex.org/W3016101097","https://openalex.org/W3080913164","https://openalex.org/W2949315143"],"abstract_inverted_index":{"Machine":[0],"learning":[1],"systems":[2,10,76],"and":[3,77,94,111],"also,":[4],"specifically,":[5],"automatic":[6],"speech":[7],"recognition":[8,181],"(ASR)":[9],"are":[11,33,134,143],"vulnerable":[12],"against":[13],"adversarial":[14,147],"attacks,":[15,35],"where":[16],"an":[17,38,51,150],"attacker":[18,39],"maliciously":[19],"changes":[20],"the":[21,24,29,43,64,120,153,170,173,184],"input.":[22],"In":[23,68],"case":[25],"of":[26,58,66,119,158,183],"ASR":[27,75,194],"systems,":[28],"most":[30],"interesting":[31],"cases":[32],"targeted":[34],"in":[36,50,178,188],"which":[37,175],"aims":[40],"to":[41,85,123,145,172,190],"force":[42],"system":[44],"into":[45],"recognizing":[46],"given":[47],"target":[48,186],"transcriptions":[49],"arbitrary":[52],"audio":[53],"sample.":[54],"The":[55,162],"increasing":[56],"number":[57],"sophisticated,":[59],"quasi":[60],"imperceptible":[61],"attacks":[62],"raises":[63],"question":[65],"countermeasures.":[67],"this":[69,140],"paper,":[70],"we":[71,142],"focus":[72],"on":[73,139],"hybrid":[74,193],"compare":[78],"four":[79],"acoustic":[80,121],"models":[81],"regarding":[82],"their":[83],"ability":[84],"indicate":[86],"uncertainty":[87,101,117,166],"under":[88,152],"attack:":[89],"a":[90,104,112,125,179,191],"feed-forward":[91],"neural":[92,96,106,163],"network":[93],"three":[95],"networks":[97,164],"specifically":[98],"designed":[99],"for":[100,130,165],"quantification,":[102],"namely":[103],"Bayesian":[105],"network,":[107],"Monte":[108],"Carlo":[109],"dropout,":[110],"deep":[113],"ensemble.":[114],"We":[115],"employ":[116],"measures":[118],"model":[122,129],"construct":[124],"simple":[126],"one-class":[127],"classification":[128],"assessing":[131],"whether":[132],"inputs":[133],"benign":[135],"or":[136],"adversarial.":[137],"Based":[138],"approach,":[141],"able":[144],"detect":[146],"examples":[148],"with":[149],"area":[151],"receiving":[154],"operator":[155],"curve":[156],"score":[157],"more":[159],"than":[160],"0.99.":[161],"quantification":[167],"simultaneously":[168],"diminish":[169],"vulnerability":[171],"attack,":[174],"is":[176],"reflected":[177],"lower":[180],"accuracy":[182],"malicious":[185],"text":[187],"comparison":[189],"standard":[192],"system.":[195]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-17T09:13:05.818461","created_date":"2020-06-05T00:00:00"}
