{"id":"https://openalex.org/W7160399915","doi":"https://doi.org/10.48550/arxiv.2605.03390","title":"Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework","display_name":"Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160399915","doi":"https://doi.org/10.48550/arxiv.2605.03390"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03390","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.2605.03390","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135454191","display_name":"Ke Liu","orcid":"https://orcid.org/0000-0001-9812-4172"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135431985","display_name":"Jiwei Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Jiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135517655","display_name":"Shuchang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Shuchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122751869","display_name":"Yutong Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Yutong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117666173","display_name":"Ruikun Chai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chai, Ruikun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135516186","display_name":"Yitong Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Yitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135492344","display_name":"Yuyang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yuyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135528529","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0002-9575-4512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yang","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":0.27320000529289246,"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":0.27320000529289246,"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":0.1379999965429306,"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/T11448","display_name":"Face recognition and analysis","score":0.10790000110864639,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.9398999810218811},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6840999722480774},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.667900025844574},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.46869999170303345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3824000060558319}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9398999810218811},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6840999722480774},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.667900025844574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6136999726295471},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5210999846458435},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3684999942779541},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3093000054359436},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03390","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.2605.03390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03390","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":[{"display_name":"Reduced inequalities","score":0.7449867725372314,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Supervised":[0],"talking":[1,180],"head":[2,181],"forgery":[3,21,182],"detection":[4],"faces":[5],"severe":[6],"generalization":[7],"challenges":[8],"due":[9],"to":[10,95,120,139],"the":[11,40,55,82,98,112,132,141,148,163,171],"continuous":[12],"evolution":[13],"of":[14,43,85,102,114,144],"generators.":[15],"By":[16],"reducing":[17],"reliance":[18],"on":[19,35,59],"generator-specific":[20],"patterns,":[22],"self-supervised":[23,53,105,179],"detectors":[24,45,183],"offer":[25],"stronger":[26,37],"cross-generator":[27],"robustness.":[28],"However,":[29],"existing":[30,103,178],"research":[31],"has":[32],"mainly":[33,166],"focused":[34],"building":[36],"detectors,":[38,54],"while":[39],"discriminative":[41,57,100,187],"capacity":[42,101],"trained":[44],"remains":[46],"insufficiently":[47],"exploited.":[48],"In":[49],"particular,":[50],"for":[51,71],"score-based":[52,104],"limited":[56],"ability":[58],"hard":[60],"cases":[61],"is":[62],"often":[63],"reflected":[64],"in":[65],"unreliable":[66],"anomaly":[67],"ordering,":[68],"leaving":[69],"room":[70],"further":[72,96],"refinement.":[73],"Motivated":[74],"by":[75],"this":[76],"observation,":[77],"we":[78],"draw":[79],"inspiration":[80],"from":[81,167],"dual-system":[83,196],"theory":[84],"human":[86],"cognition":[87],"and":[88,125,159],"propose":[89],"a":[90],"Training-Free":[91],"Dual-System":[92],"(TFDS)":[93],"framework":[94],"exploit":[97],"latent":[99],"detectors.":[106],"TFDS":[107],"treats":[108],"anomaly-like":[109],"scores":[110],"as":[111],"basis":[113],"System-1,":[115],"using":[116],"lightweight":[117],"threshold-based":[118],"routing":[119],"partition":[121],"samples":[122,146],"into":[123],"confident":[124],"uncertain":[126,133,172],"subsets.":[127],"System-2":[128],"then":[129],"revisits":[130],"only":[131],"subset,":[134],"performing":[135],"fine-grained":[136],"evidence-guided":[137],"reasoning":[138],"refine":[140],"relative":[142],"ordering":[143,169],"ambiguous":[145],"within":[147,170],"original":[149],"score":[150],"distribution.":[151],"Extensive":[152],"experiments":[153],"demonstrate":[154],"consistent":[155],"improvements":[156],"across":[157],"datasets":[158],"perturbation":[160],"settings,":[161],"with":[162],"gains":[164],"arising":[165],"corrected":[168],"subset.":[173],"These":[174],"findings":[175],"show":[176],"that":[177,189],"still":[184],"contain":[185],"underexploited":[186],"cues":[188],"can":[190],"be":[191],"effectively":[192],"unlocked":[193],"through":[194],"training-free":[195],"reasoning.":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-07T00:00:00"}
