{"id":"https://openalex.org/W7131133877","doi":"https://doi.org/10.1109/iccvw69036.2025.00599","title":"Practical Manipulation Model for Robust Deepfake Detection","display_name":"Practical Manipulation Model for Robust Deepfake Detection","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131133877","doi":"https://doi.org/10.1109/iccvw69036.2025.00599"},"language":null,"primary_location":{"id":"doi:10.1109/iccvw69036.2025.00599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00599","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120646383","display_name":"Benedikt Hopf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Benedikt Hopf","raw_affiliation_strings":["University of W&#x00FC;rzburg,Computer Vision Lab, CAIDAS &#x0026; IFI,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of W&#x00FC;rzburg,Computer Vision Lab, CAIDAS &#x0026; IFI,Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124009091","display_name":"Radu Timofte","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Radu Timofte","raw_affiliation_strings":["University of W&#x00FC;rzburg,Computer Vision Lab, CAIDAS &#x0026; IFI,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of W&#x00FC;rzburg,Computer Vision Lab, CAIDAS &#x0026; IFI,Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65649774,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5734","last_page":"5743"},"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":0.9351000189781189,"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":0.9351000189781189,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.00989999994635582,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.006899999920278788,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/robustness","display_name":"Robustness (evolution)","score":0.8790000081062317},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.7085000276565552},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5809000134468079},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48579999804496765},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.40959998965263367},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.37540000677108765},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.3221000134944916},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.32120001316070557}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8790000081062317},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.72079998254776},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.7085000276565552},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5809000134468079},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48579999804496765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4837999939918518},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48159998655319214},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37459999322891235},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3221000134944916},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28110000491142273},{"id":"https://openalex.org/C3019813237","wikidata":"https://www.wikidata.org/wiki/Q65089264","display_name":"Model validation","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25920000672340393},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00599","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2070604790","https://openalex.org/W2301937176","https://openalex.org/W2616247523","https://openalex.org/W2891145043","https://openalex.org/W2942074357","https://openalex.org/W2982058372","https://openalex.org/W3017837134","https://openalex.org/W3034196597","https://openalex.org/W3034577585","https://openalex.org/W3034713808","https://openalex.org/W3089994210","https://openalex.org/W3094728142","https://openalex.org/W3108854358","https://openalex.org/W3136860328","https://openalex.org/W3174656926","https://openalex.org/W3176241004","https://openalex.org/W3187526215","https://openalex.org/W3203631022","https://openalex.org/W4297580547","https://openalex.org/W4297991541","https://openalex.org/W4312388562","https://openalex.org/W4312933868","https://openalex.org/W4312967678","https://openalex.org/W4313127140","https://openalex.org/W4390871965","https://openalex.org/W4390872011","https://openalex.org/W4390873305","https://openalex.org/W4402715910","https://openalex.org/W4402716253","https://openalex.org/W4402727591"],"related_works":[],"abstract_inverted_index":{"Modern":[0],"deepfake":[1],"detection":[2,15],"models":[3],"have":[4,53],"achieved":[5],"strong":[6,95],"performance":[7,16,120],"even":[8],"on":[9,25,121,137],"the":[10,38,47,70,88,98,110,138,145,152],"challenging":[11],"cross-dataset":[12],"task.":[13],"However,":[14],"under":[17],"non-ideal":[18],"conditions":[19],"remains":[20],"very":[21],"unstable,":[22],"limiting":[23],"success":[24],"some":[26],"benchmark":[27,123],"datasets":[28],"and":[29,83,91,133,140,159],"making":[30],"it":[31],"easy":[32],"to":[33,40,97,113],"circumvent":[34],"detection.":[35],"Inspired":[36],"by":[37,74,93],"move":[39],"a":[41,55,62],"more":[42,78],"real-world":[43],"degradation":[44],"model":[45],"in":[46,156,162],"area":[48],"of":[49,65,72,130,154],"image":[50,115],"super-resolution,":[51],"we":[52,86,126,150],"developed":[54],"Practical":[56],"Manipulation":[57],"Model":[58],"(PMM)":[59],"that":[60,103],"covers":[61],"larger":[63],"set":[64],"possible":[66],"forgeries.":[67],"We":[68,101],"extend":[69],"space":[71],"pseudo-fakes":[73],"using":[75],"Poisson":[76],"blending,":[77],"diverse":[79],"masks,":[80],"generator":[81],"artifacts,":[82],"distractors.":[84],"Additionally,":[85],"improve":[87,119],"detectors'":[89],"generality":[90],"robustness":[92,112,155],"adding":[94],"degradations":[96,116],"training":[99],"images.":[100],"demonstrate":[102],"these":[104],"changes":[105],"not":[106],"only":[107],"significantly":[108],"enhance":[109],"model's":[111],"common":[114],"but":[117],"also":[118],"standard":[122],"datasets.":[124],"Specifically,":[125],"show":[127],"clear":[128],"increases":[129],"3.51":[131],"%":[132,135],"6.21":[134],"AUC":[136],"DFDC":[139],"DFDCP":[141],"datasets,":[142],"respectively,":[143],"over":[144],"s-o-t-a":[146],"LAA":[147],"backbone.":[148],"Furthermore,":[149],"highlight":[151],"lack":[153],"previous":[157],"detectors":[158],"our":[160],"improvements":[161],"this":[163],"regard.":[164],"Code":[165],"can":[166],"be":[167],"found":[168],"at":[169],"https://github.com/BenediktHopf/PMM.":[170]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-24T00:00:00"}
