{"id":"https://openalex.org/W7157568409","doi":"https://doi.org/10.48550/arxiv.2604.25889","title":"Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles","display_name":"Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles","publication_year":2026,"publication_date":"2026-04-28","ids":{"openalex":"https://openalex.org/W7157568409","doi":"https://doi.org/10.48550/arxiv.2604.25889"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.25889","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25889","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":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.2604.25889","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118801425","display_name":"Minh-Khoa Le-Phan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le-Phan, Minh-Khoa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126782283","display_name":"Minh-Hoang Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Minh-Hoang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134874983","display_name":"Trong-Le Do","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Do, Trong-Le","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134830756","display_name":"Minh-Triet Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Minh-Triet","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":0.8489999771118164,"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":0.8489999771118164,"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/T11019","display_name":"Image Enhancement Techniques","score":0.03530000150203705,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.015399999916553497,"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/feature","display_name":"Feature (linguistics)","score":0.5248000025749207},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5210999846458435},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4803999960422516},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.43320000171661377},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43130001425743103},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.38690000772476196},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.38269999623298645},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3779999911785126},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.3682999908924103},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.367000013589859}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6965000033378601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5439000129699707},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5248000025749207},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.43320000171661377},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42879998683929443},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.38269999623298645},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3779999911785126},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.367000013589859},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.323199987411499},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31929999589920044},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31279999017715454},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.31119999289512634},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.2816999852657318},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26669999957084656},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.25889","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25889","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":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.2604.25889","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25889","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4331970512866974,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Current":[0],"deepfake":[1],"detection":[2],"models":[3],"achieve":[4],"state-of-the-art":[5],"performance":[6],"on":[7],"pristine":[8],"academic":[9],"datasets":[10],"but":[11],"suffer":[12],"severe":[13,25],"spatial":[14,97],"attention":[15,120,137],"drift":[16,138],"under":[17],"real-world":[18],"compound":[19,42],"degradations,":[20],"such":[21],"as":[22,141],"blurring":[23],"and":[24,68,87,101,118],"lossy":[26],"compression.":[27],"To":[28],"address":[29],"this":[30],"vulnerability,":[31],"we":[32,107],"propose":[33],"a":[34,46,79,83,88,127,142],"foundation-driven":[35],"forensic":[36],"framework":[37],"that":[38,110],"integrates":[39],"an":[40],"extreme":[41],"degradation":[43,54],"engine":[44],"with":[45],"structurally":[47],"constrained,":[48],"multi-stream":[49],"architecture.":[50],"During":[51],"training,":[52],"our":[53,132],"pipeline":[55],"systematically":[56],"destroys":[57],"high-frequency":[58],"artifacts,":[59],"optimizing":[60],"the":[61,157],"DINOv2-Giant":[62],"backbone":[63],"to":[64],"extract":[65,113],"invariant":[66],"geometric":[67,144],"semantic":[69],"priors.":[70],"We":[71],"then":[72],"process":[73],"images":[74],"through":[75],"three":[76],"specialized":[77],"pathways:":[78],"Global":[80],"Texture":[81],"stream,":[82,86],"Localized":[84],"Facial":[85],"Hybrid":[89],"Semantic":[90],"Fusion":[91],"stream":[92],"incorporating":[93],"CLIP.":[94],"Through":[95],"analyzing":[96],"attribution":[98],"via":[99,126],"Score-CAM":[100],"feature":[102,116],"stability":[103],"using":[104],"Cosine":[105],"Similarity,":[106],"quantitatively":[108],"demonstrate":[109],"these":[111,124],"streams":[112],"non-redundant,":[114],"complementary":[115],"representations":[117],"stabilize":[119],"entropy.":[121],"By":[122],"aggregating":[123],"predictions":[125],"calibrated,":[128],"discretized":[129],"voting":[130],"mechanism,":[131],"ensemble":[133],"successfully":[134],"suppresses":[135],"background":[136],"while":[139],"acting":[140],"robust":[143],"anchor.":[145],"Our":[146],"approach":[147],"yields":[148],"highly":[149],"stable":[150],"zero-shot":[151],"generalization,":[152],"achieving":[153],"Fourth":[154],"Place":[155],"in":[156],"NTIRE":[158],"2026":[159],"Robust":[160],"Deepfake":[161],"Detection":[162],"Challenge":[163],"at":[164,169],"CVPR.":[165],"Code":[166],"is":[167],"available":[168],"https://github.com/khoalephanminh/ntire26-deepfake-challenge.":[170]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-30T00:00:00"}
