{"id":"https://openalex.org/W7169888730","doi":"https://doi.org/10.48550/arxiv.2607.17761","title":"Time-Frequency Consistency Learning for Robust Speech Deepfake Detection","display_name":"Time-Frequency Consistency Learning for Robust Speech Deepfake Detection","publication_year":2026,"publication_date":"2026-07-20","ids":{"openalex":"https://openalex.org/W7169888730","doi":"https://doi.org/10.48550/arxiv.2607.17761"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.17761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.17761","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.17761","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141230341","display_name":"Jun Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124403526","display_name":"Zhuolin Yi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi, Zhuolin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141238430","display_name":"Yanzhen Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Yanzhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141312118","display_name":"Yihuan Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yihuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102123841","display_name":"Jiayu Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Jiayu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141305095","display_name":"Yi Chai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chai, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051841023","display_name":"Guanxiang Feng","orcid":"https://orcid.org/0000-0003-0692-2367"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Guanxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141262399","display_name":"Jiajun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jiajun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141248468","display_name":"Tong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.883899986743927,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.883899986743927,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.07829999923706055,"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/T10283","display_name":"Hearing Loss and Rehabilitation","score":0.0044999998062849045,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7580999732017517},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4749000072479248},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.448199987411499},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42309999465942383},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3749000132083893},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3743000030517578},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.3547999858856201},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3231000006198883},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.3172999918460846}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7580999732017517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7095999717712402},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.478300005197525},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4749000072479248},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.43560001254081726},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42309999465942383},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3547999858856201},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3231000006198883},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3172999918460846},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C167900197","wikidata":"https://www.wikidata.org/wiki/Q11081100","display_name":"Spoofing attack","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.301800012588501},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.2662000060081482},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C177502760","wikidata":"https://www.wikidata.org/wiki/Q782524","display_name":"Automatic gain control","level":4,"score":0.26170000433921814},{"id":"https://openalex.org/C2778701210","wikidata":"https://www.wikidata.org/wiki/Q28130034","display_name":"Constructive","level":3,"score":0.26170000433921814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.17761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.17761","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":"doi:10.48550/arxiv.2607.17761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.17761","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6148086786270142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"speech":[1],"deepfake":[2],"detection":[3,59,85],"(SDD)":[4],"has":[5],"achieved":[6],"significant":[7],"progress.":[8],"However,":[9],"its":[10],"robustness":[11,199],"evaluation":[12,65],"remains":[13],"largely":[14],"confined":[15],"to":[16,101,147,157,168],"controlled":[17],"additive":[18],"noise":[19,51],"scenarios,":[20],"lacking":[21],"systematic":[22],"investigation":[23],"of":[24,66,200],"the":[25,74,164,184,189,198],"complex":[26],"distortions":[27,79],"introduced":[28,80],"by":[29,81,127,193],"acoustic":[30,48],"front-end":[31],"(AFE)":[32],"processing":[33],"pipelines":[34],"in":[35,202],"real-world":[36,203],"deployments.":[37],"In":[38],"this":[39,89,138],"work,":[40],"we":[41,91],"simulate":[42],"a":[43,63,93,162],"unified":[44],"AFE":[45,82,112,117,194],"pipeline":[46],"comprising":[47],"echo":[49],"cancellation,":[50],"suppression,":[52],"automatic":[53],"gain":[54],"control,":[55],"and":[56,61,76,110,176],"voice":[57],"activity":[58],"(VAD),":[60],"conduct":[62],"comprehensive":[64],"current":[67],"state-of-the-art":[68],"models.":[69],"The":[70,205],"results":[71,181],"show":[72],"that":[73,106,116,183],"nonlinear":[75],"time-frequency":[77],"coupled":[78],"significantly":[83,196],"degrade":[84],"performance.":[86],"To":[87,137],"address":[88],"issue,":[90],"propose":[92],"Time-Frequency":[94],"Consistency":[95],"Learning":[96],"(TFCL)":[97],"framework,":[98],"which":[99],"aims":[100],"learn":[102],"invariant":[103],"spoofing":[104],"representations":[105,171],"remain":[107],"stable":[108,170],"before":[109],"after":[111],"processing.":[113],"We":[114],"observe":[115],"not":[118],"only":[119],"introduces":[120],"temporal":[121,174],"misalignment":[122],"(e.g.,":[123],"segment-level":[124],"shifts":[125],"caused":[126,192],"VAD),":[128],"but":[129],"also":[130],"weakens":[131],"or":[132],"distorts":[133],"critical":[134],"frequency-domain":[135,153],"cues.":[136],"end,":[139],"TFCL":[140],"employs":[141],"an":[142],"attention-driven":[143],"soft":[144],"alignment":[145],"mechanism":[146],"capture":[148],"cross-temporal":[149],"dependencies,":[150],"along":[151],"with":[152],"structural":[154],"consistency":[155],"constraints":[156],"enforce":[158],"feature":[159],"invariance.":[160],"As":[161],"result,":[163],"model":[165],"is":[166,207],"able":[167],"maintain":[169],"under":[172],"both":[173],"perturbations":[175],"spectral":[177],"distortions.":[178],"Extensive":[179],"experimental":[180],"demonstrate":[182],"proposed":[185],"method":[186],"effectively":[187],"mitigates":[188],"performance":[190],"degradation":[191],"processing,":[195],"improving":[197],"SDD":[201],"scenarios.":[204],"code":[206],"available":[208],"at":[209],"https://github.com/JunXue-tech/TFCL.":[210]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-22T00:00:00"}