{"id":"https://openalex.org/W7118934411","doi":"https://doi.org/10.48550/arxiv.2601.02359","title":"ExposeAnyone: Personalized Audio-to-Expression Diffusion Models Are Robust Zero-Shot Face Forgery Detectors","display_name":"ExposeAnyone: Personalized Audio-to-Expression Diffusion Models Are Robust Zero-Shot Face Forgery Detectors","publication_year":2026,"publication_date":"2026-01-05","ids":{"openalex":"https://openalex.org/W7118934411","doi":"https://doi.org/10.48550/arxiv.2601.02359"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.02359","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.02359","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.2601.02359","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078203902","display_name":"Kaede Shiohara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiohara, Kaede","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122240869","display_name":"Toshihiko Yamasaki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yamasaki, Toshihiko","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080103406","display_name":"Vladislav Golyanik","orcid":"https://orcid.org/0000-0003-1630-2006"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golyanik, Vladislav","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.4205999970436096,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.4205999970436096,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11448","display_name":"Face recognition and analysis","score":0.2849999964237213,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.08070000261068344,"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/overfitting","display_name":"Overfitting","score":0.892799973487854},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.788100004196167},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6766999959945679},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5116999745368958},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4959999918937683},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.45100000500679016},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.3804999887943268},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3197999894618988}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.892799973487854},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.788100004196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7185999751091003},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.705299973487854},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6766999959945679},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5116999745368958},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4959999918937683},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.45100000500679016},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4348999857902527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4154999852180481},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3804999887943268},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3197999894618988},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.2770000100135803},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.02359","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.02359","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.2601.02359","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.02359","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":[{"score":0.6739975810050964,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Detecting":[0],"unknown":[1],"deepfake":[2],"manipulations":[3],"remains":[4],"one":[5],"of":[6,154],"the":[7,90,103,129,138,159,179],"most":[8],"challenging":[9],"problems":[10],"in":[11,137,181],"face":[12,118,183],"forgery":[13,42,119,184],"detection.":[14,120,185],"Current":[15],"state-of-the-art":[16,131],"approaches":[17,161],"fail":[18],"to":[19,21,38,40,57,94,171],"generalize":[20],"unseen":[22],"manipulations,":[23],"as":[24,174],"they":[25],"primarily":[26],"rely":[27],"on":[28,75,141],"supervised":[29],"training":[30],"with":[31],"existing":[32,54],"deepfakes":[33],"or":[34],"pseudo-fakes,":[35],"which":[36],"leads":[37],"overfitting":[39],"specific":[41,95],"patterns.":[43],"In":[44,64],"contrast,":[45],"self-supervised":[46,72],"methods":[47],"offer":[48],"greater":[49],"potential":[50],"for":[51],"generalization,":[52],"but":[53],"work":[55],"struggles":[56],"learn":[58],"discriminative":[59],"representations":[60],"only":[61],"from":[62,83],"self-supervision.":[63],"this":[65],"paper,":[66],"we":[67],"propose":[68],"ExposeAnyone,":[69],"a":[70,76],"fully":[71],"approach":[73],"based":[74],"diffusion":[77,113],"model":[78,91,150],"that":[79,124],"generates":[80],"expression":[81],"sequences":[82],"audio.":[84],"The":[85],"key":[86],"idea":[87],"is,":[88],"once":[89],"is":[92,151,168],"personalized":[93,110],"subjects":[96,111],"using":[97],"reference":[98],"sets,":[99],"it":[100],"can":[101],"compute":[102],"identity":[104],"distances":[105],"between":[106],"suspected":[107],"videos":[108],"and":[109,145,164,176],"via":[112],"reconstruction":[114],"errors,":[115],"enabling":[116],"person-of-interest":[117],"Extensive":[121],"experiments":[122],"demonstrate":[123],"1)":[125],"our":[126,149,166],"method":[127,132,167],"outperforms":[128],"previous":[130,160],"by":[133],"4.22":[134],"percentage":[135],"points":[136],"average":[139],"AUC":[140],"DF-TIMIT,":[142],"DFDCP,":[143],"KoDF,":[144],"IDForge":[146],"datasets,":[147],"2)":[148],"also":[152],"capable":[153],"detecting":[155],"Sora2-generated":[156],"videos,":[157],"where":[158],"perform":[162],"poorly,":[163],"3)":[165],"highly":[169],"robust":[170],"corruptions":[172],"such":[173],"blur":[175],"compression,":[177],"highlighting":[178],"applicability":[180],"real-world":[182]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-01-08T00:00:00"}
