{"id":"https://openalex.org/W7225757861","doi":"https://doi.org/10.5281/zenodo.24097045","title":"**VeriMedia: A Spatial-Temporal Deep Learning Platform for Video Deepfake Detection and Forensic Verification**","display_name":"**VeriMedia: A Spatial-Temporal Deep Learning Platform for Video Deepfake Detection and Forensic Verification**","publication_year":2026,"publication_date":"2026-11-10","ids":{"openalex":"https://openalex.org/W7225757861","doi":"https://doi.org/10.5281/zenodo.24097045"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24097045","doi":"https://doi.org/10.5281/zenodo.24097045","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24097045","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24097045","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161389233","display_name":"Sushrut Anil Lajurkar lajurkar","orcid":"https://orcid.org/0009-0003-2687-5702","observed_orcids":["https://orcid.org/0009-0003-2687-5702"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"lajurkar, Sushrut Anil Lajurkar","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0003-2687-5702","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5161389233"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T12357","display_name":"Digital Media Forensic Detection","score":1.0,"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/T12357","display_name":"Digital Media Forensic Detection","score":1.0,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8134999871253967},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7838000059127808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7003999948501587},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.699400007724762},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4262000024318695},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42260000109672546},{"id":"https://openalex.org/C63000827","wikidata":"https://www.wikidata.org/wiki/Q3080428","display_name":"Software portability","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35690000653266907},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3337000012397766},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.33340001106262207},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.31839999556541443},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27970001101493835},{"id":"https://openalex.org/C84418412","wikidata":"https://www.wikidata.org/wiki/Q3246940","display_name":"Digital forensics","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24097045","doi":"https://doi.org/10.5281/zenodo.24097045","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24097045","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.5281/zenodo.24097046","doi":"https://doi.org/10.5281/zenodo.24097046","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24097046","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24097045","doi":"https://doi.org/10.5281/zenodo.24097045","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24097045","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,150,212,247,292],"rapid":[1],"advancement":[2],"of":[3,39,83],"generative":[4,66],"artificial":[5,329],"intelligence":[6],"has":[7],"made":[8],"it":[9],"increasingly":[10],"difficult":[11],"to":[12,30,64,121,136,146,194,218,238,295,314,333,340],"distinguish":[13],"authentic":[14],"digital":[15,31,77,301,311],"videos":[16],"from":[17,57,178],"synthetically":[18],"worked":[19,134],"content,":[20,313],"commonly":[21],"known":[22],"as":[23],"deepfakes.":[24],"These":[25,181],"tweaks":[26],"pose":[27],"noticeable":[28],"obstacles":[29],"media":[32,305],"authenticity,":[33],"cybersecurity,":[34],"privacy":[35],"and":[36,70,80,113,125,166,204,226,243,265,271,284,300,307,327,337],"the":[37,81,227,233,290],"reliability":[38],"visual":[40,138,312],"proof,":[41],"in":[42,73,85,116,135,303,310],"real-world":[43],"scenarios.":[44],"In":[45,76],"fact,":[46],"standard":[47],"deepfake":[48,111,342],"detection":[49,112,240],"approaches":[50],"mostly":[51],"depend":[52],"on":[53,289],"spatial":[54,175,324],"attributes":[55],"pulled":[56],"individual":[58,179],"video":[59,68,110,235,274,341],"frames,":[60],"making":[61],"them":[62],"vulnerable":[63],"advanced":[65],"procedures,":[67],"compression,":[69],"post-processing":[71],"operations,":[72],"many":[74],"respects.":[75],"forensic":[78,114,126,269,286,297],"inquiries,":[79],"lack":[82],"interpretability":[84],"existing":[86],"deep":[87,106],"learning":[88,107],"models":[89],"is":[90,102,133,154,215,249,272],"limiting":[91],"their":[92],"applicability.":[93],"To":[94],"tackle":[95],"these":[96],"issues,":[97],"what":[98],"this":[99],"paper":[100],"suggests":[101],"VeriMedia,":[103],"a":[104,147,170,187,253,257,315,335],"spatial-temporal":[105],"platform":[108,248,293],"for":[109,161,173],"verification,":[115],"actual":[117],"application.":[118],"And":[119],"thus,":[120],"sharpen":[122],"model":[123],"transparency":[124],"interpretability,":[127],"Gradient-weighted":[128],"Class":[129],"Activation":[130],"Mapping":[131],"(Grad-CAM)":[132],"make":[137],"saliency":[139],"heatmaps":[140],"accentuating":[141],"potentially":[142],"controlled":[143],"facial":[144,162,200],"regions,":[145],"significant":[148,316],"degree.":[149,317],"put":[151],"forward":[152],"architecture":[153,255],"blending":[155],"Multi-Task":[156],"Cascaded":[157],"Convolutional":[158],"Networks":[159],"(MTCNN)":[160],"detection,":[163,343],"landmark":[164],"alignment,":[165],"preprocessing,":[167],"followed":[168],"by":[169,322],"ResNeXt-50":[171],"backbone":[172],"pulling":[174],"feature":[176,325],"representations":[177],"frames.":[180],"traits":[182],"are":[183],"later":[184],"handled":[185],"employing":[186,221],"Bidirectional":[188],"Long":[189],"Short-Term":[190],"Memory":[191],"(Bi-LSTM)":[192],"network":[193],"encapsulate":[195],"temporal":[196,281,319],"inconsistencies,":[197],"covering":[198,224],"unnatural":[199],"movements,":[201],"inter-frame":[202],"flickering,":[203],"irregular":[205],"transitions":[206],"that":[207],"may":[208],"show":[209],"synthetic":[210],"tweak.":[211],"suggested":[213],"system":[214],"mainly":[216],"intended":[217],"be":[219],"checked":[220],"benchmark":[222],"datasets,":[223],"FaceForensics++":[225],"Deepfake":[228],"Detection":[229],"Challenge":[230],"(DFDC),":[231],"under":[232],"distinct":[234],"compression":[236],"conditions":[237],"assess":[239],"accuracy,":[241],"generalization":[242],"robustness,":[244],"broadly":[245,344],"speaking.":[246,345],"currently":[250],"designed":[251],"with":[252],"modular":[254],"including":[256],"FastAPI-based":[258],"asynchronous":[259],"backend,":[260],"Redis-powered":[261],"background":[262],"task":[263],"processing,":[264],"an":[266],"interactive":[267],"React-based":[268],"dashboard,":[270],"supporting":[273],"ingestion,":[275],"frame-level":[276],"examination,":[277],"manipulation":[278],"probability":[279],"estimation,":[280],"anomaly":[282],"visualization,":[283],"automated":[285],"report":[287],"generation,":[288],"whole.":[291],"seeks":[294],"support":[296],"analysts,":[298],"journalists,":[299],"investigators":[302],"assessing":[304],"authenticity":[306],"strengthening":[308],"trust":[309],"Indeed,":[318],"sequence":[320],"modelling":[321],"combining":[323],"extraction,":[326],"explainable":[328],"intelligence,":[330],"VeriMedia":[331],"aims":[332],"offer":[334],"broad":[336],"interpretable":[338],"method":[339]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-11T00:00:00"}