{"id":"https://openalex.org/W4402352499","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650300","title":"SpeechGuard: Online Defense against Backdoor Attacks on Speech Recognition Models","display_name":"SpeechGuard: Online Defense against Backdoor Attacks on Speech Recognition Models","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352499","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650300"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650300","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/2607.15697","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062774626","display_name":"Jinwen Xin","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinwen Xin","raw_affiliation_strings":["Xidian University,School of Cyber Engineering,Xi’an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Cyber Engineering,Xi’an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059296628","display_name":"Xixiang Lv","orcid":"https://orcid.org/0000-0003-2879-241X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xixiang Lv","raw_affiliation_strings":["Xidian University,School of Cyber Engineering,Xi’an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Cyber Engineering,Xi’an,China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14400694,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9997000098228455,"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":0.9997000098228455,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9975000023841858,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9959999918937683,"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/backdoor","display_name":"Backdoor","score":0.982169508934021},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7136799097061157},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5221393704414368},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32970839738845825},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.27917245030403137}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.982169508934021},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7136799097061157},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5221393704414368},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32970839738845825},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.27917245030403137}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650300","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2607.15697","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.15697","pdf_url":"https://arxiv.org/pdf/2607.15697","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,"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.2607.15697","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15697","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,"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2607.15697","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.15697","pdf_url":"https://arxiv.org/pdf/2607.15697","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,"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":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402352499.pdf","grobid_xml":"https://content.openalex.org/works/W4402352499.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W2753783305","https://openalex.org/W2774423163","https://openalex.org/W2797583228","https://openalex.org/W2807363941","https://openalex.org/W2872172641","https://openalex.org/W2888641632","https://openalex.org/W2934843808","https://openalex.org/W2942091739","https://openalex.org/W2962858109","https://openalex.org/W2971661634","https://openalex.org/W2990138404","https://openalex.org/W2990270730","https://openalex.org/W3015241525","https://openalex.org/W3042368254","https://openalex.org/W3048636285","https://openalex.org/W3081178496","https://openalex.org/W3107337211","https://openalex.org/W3112001526","https://openalex.org/W3114838227","https://openalex.org/W3119520312","https://openalex.org/W3136856676","https://openalex.org/W3163083600","https://openalex.org/W4229367784","https://openalex.org/W4304091911","https://openalex.org/W4385080316","https://openalex.org/W6746897123","https://openalex.org/W6750665317","https://openalex.org/W6753441104","https://openalex.org/W6803053407"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W4320031223","https://openalex.org/W4200629851","https://openalex.org/W4281902577","https://openalex.org/W4309417370","https://openalex.org/W4292107232","https://openalex.org/W3009072493","https://openalex.org/W4386080799","https://openalex.org/W3140988292"],"abstract_inverted_index":{"Backdoor":[0],"attacks":[1,43],"pose":[2],"a":[3,14,22,180],"critical":[4],"threat":[5,177],"to":[6,12,80,92,97,121],"neural":[7],"network":[8],"models,":[9],"allowing":[10],"attackers":[11],"implant":[13],"backdoor":[15,42,53,76,141,176],"during":[16],"the":[17,26,39,65,73,111,123,140,143,175],"training":[18,27],"phase":[19],"by":[20],"manipulating":[21],"small":[23],"portion":[24],"of":[25,41,67,113,125],"data.":[28],"In":[29],"security-sensitive":[30],"applications":[31],"such":[32],"as":[33,104],"voice":[34],"interaction":[35],"for":[36,56],"autonomous":[37],"driving,":[38],"presence":[40],"introduces":[44],"substantial":[45],"security":[46],"risks.":[47],"This":[48],"study":[49],"focuses":[50],"on":[51,133],"implementing":[52],"defense":[54,77],"measures":[55],"speech":[57,151],"recognition":[58],"models":[59],"in":[60,142],"run-time,":[61],"taking":[62],"into":[63],"account":[64],"characteristics":[66],"audio":[68,85],"signals.":[69],"We":[70,116],"propose":[71],"SpeechGuard,":[72],"first":[74],"online":[75],"pipeline":[78],"designed":[79],"identify":[81],"and":[82,99,128,148],"purify":[83],"poisoned":[84,101,114,167],"samples.":[86,115,168],"Specifically,":[87],"we":[88,108],"improve":[89],"STRIP":[90],"method":[91],"perform":[93],"adaptive":[94],"perturbation":[95],"injection":[96],"detect":[98],"filter":[100,165],"samples,":[102],"named":[103],"S-STRIP.":[105],"More":[106],"importantly,":[107],"further":[109],"consider":[110],"purification":[112],"utilize":[117],"time-frequency":[118],"(T-F)":[119],"masking":[120],"suppress":[122],"expression":[124],"trigger":[126],"signals":[127],"autonomously":[129],"generate":[130],"masks":[131],"based":[132],"an":[134],"autoencoder.":[135],"The":[136],"two-stage":[137],"processing":[138],"prevents":[139],"model":[144],"from":[145],"being":[146],"triggered,":[147],"even":[149],"input":[150],"carrying":[152],"triggers":[153],"can":[154,163,172],"be":[155],"accurately":[156,164],"predicted.":[157],"Extensive":[158],"experimental":[159],"demonstrate":[160],"that":[161],"SpeechGuard":[162],"out":[166],"Through":[169],"purification,":[170],"it":[171],"significantly":[173],"mitigate":[174],"while":[178],"maintaining":[179],"certain":[181],"prediction":[182],"accuracy.":[183]},"counts_by_year":[],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2025-10-10T00:00:00"}