{"id":"https://openalex.org/W4416050289","doi":"https://doi.org/10.48550/arxiv.2508.14980","title":"Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection","display_name":"Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection","publication_year":2025,"publication_date":"2025-08-20","ids":{"openalex":"https://openalex.org/W4416050289","doi":"https://doi.org/10.48550/arxiv.2508.14980"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2508.14980","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.14980","pdf_url":"https://arxiv.org/pdf/2508.14980","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.14980","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092838089","display_name":"Andrei Balykin","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Balykin, Andrei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120318263","display_name":"Anvar Ganiev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ganiev, Anvar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009936205","display_name":"\u0414\u0435\u043d\u0438\u0441 \u041a\u043e\u043d\u0434\u0440\u0430\u043d\u0438\u043d","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kondranin, Denis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005350796","display_name":"Kirill Polevoda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Polevoda, Kirill","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120318264","display_name":"Nikolai Liudkevich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liudkevich, Nikolai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5089884362","display_name":"Artem Petrov","orcid":"https://orcid.org/0000-0001-5172-4007"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Petrov, Artem","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5092838089"],"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/T10828","display_name":"Biometric Identification and Security","score":0.9559999704360962,"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/T10828","display_name":"Biometric Identification and Security","score":0.9559999704360962,"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/T11448","display_name":"Face recognition and analysis","score":0.026799999177455902,"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/T11800","display_name":"User Authentication and Security Systems","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/liveness","display_name":"Liveness","score":0.8149999976158142},{"id":"https://openalex.org/keywords/spoofing-attack","display_name":"Spoofing attack","score":0.6686999797821045},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.46470001339912415},{"id":"https://openalex.org/keywords/attack-model","display_name":"Attack model","score":0.4603999853134155},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.42730000615119934},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.3560999929904938},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.3504999876022339},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.34380000829696655},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.3409000039100647}],"concepts":[{"id":"https://openalex.org/C15569618","wikidata":"https://www.wikidata.org/wiki/Q3561421","display_name":"Liveness","level":2,"score":0.8149999976158142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7860999703407288},{"id":"https://openalex.org/C167900197","wikidata":"https://www.wikidata.org/wiki/Q11081100","display_name":"Spoofing attack","level":2,"score":0.6686999797821045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5996000170707703},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.4603999853134155},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.42730000615119934},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3601999878883362},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.3560999929904938},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.33719998598098755},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3240000009536743},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31119999289512634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29829999804496765},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2549000084400177}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2508.14980","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.14980","pdf_url":"https://arxiv.org/pdf/2508.14980","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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.2508.14980","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.14980","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.14980","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.14980","pdf_url":"https://arxiv.org/pdf/2508.14980","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"face":[1],"recognition":[2],"systems":[3,47],"remain":[4],"vulnerable":[5],"to":[6,49,73],"spoofing":[7],"attempts,":[8],"including":[9],"both":[10],"physical":[11],"presentation":[12],"attacks":[13],"and":[14,33,42,45,70,111,124],"digital":[15],"forgeries.":[16],"Traditionally,":[17],"these":[18],"two":[19],"attack":[20,51,71],"vectors":[21],"have":[22],"been":[23],"handled":[24],"by":[25],"separate":[26],"models,":[27],"each":[28],"targeting":[29],"its":[30],"own":[31],"artifacts":[32],"modalities.":[34],"However,":[35],"maintaining":[36],"distinct":[37],"detectors":[38],"increases":[39],"system":[40],"complexity":[41],"inference":[43],"latency":[44],"leaves":[46],"exposed":[48],"combined":[50],"vectors.":[52],"We":[53],"propose":[54],"the":[55,80],"Paired-Sampling":[56],"Contrastive":[57],"Framework,":[58],"a":[59],"unified":[60],"training":[61],"approach":[62],"that":[63],"leverages":[64],"automatically":[65],"matched":[66],"pairs":[67],"of":[68,99],"genuine":[69],"selfies":[72],"learn":[74],"modality-agnostic":[75],"liveness":[76],"cues.":[77],"Evaluated":[78],"on":[79],"6th":[81],"Face":[82],"Anti-Spoofing":[83],"Challenge":[84],"Unified":[85],"Physical-Digital":[86],"Attack":[87],"Detection":[88],"benchmark,":[89],"our":[90],"method":[91],"achieves":[92],"an":[93],"average":[94],"classification":[95],"error":[96],"rate":[97],"(ACER)":[98],"2.10":[100],"percent,":[101],"outperforming":[102],"prior":[103],"solutions.":[104],"The":[105],"framework":[106],"is":[107],"lightweight":[108],"(4.46":[109],"GFLOPs)":[110],"trains":[112],"in":[113],"under":[114],"one":[115],"hour,":[116],"making":[117],"it":[118],"practical":[119],"for":[120],"real-world":[121],"deployment.":[122],"Code":[123],"pretrained":[125],"models":[126],"are":[127],"available":[128],"at":[129],"https://github.com/xPONYx/iccv2025_deepfake_challenge.":[130]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}