{"id":"https://openalex.org/W4293523209","doi":"https://doi.org/10.1109/icme52920.2022.9859954","title":"Human vs. Automatic Detection of Deepfake Videos Over Noisy Channels","display_name":"Human vs. Automatic Detection of Deepfake Videos Over Noisy Channels","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4293523209","doi":"https://doi.org/10.1109/icme52920.2022.9859954"},"language":"en","primary_location":{"id":"doi:10.1109/icme52920.2022.9859954","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859954","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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/A5081626228","display_name":"Swaroop Shankar Prasad","orcid":null},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Swaroop Shankar Prasad","raw_affiliation_strings":["Institute of Computer Architecture and Computer Engineering, Univeristy of Stuttgart,Germany","Institute of Computer Architecture and Computer Engineering, Univeristy of Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Architecture and Computer Engineering, Univeristy of Stuttgart,Germany","institution_ids":["https://openalex.org/I100066346"]},{"raw_affiliation_string":"Institute of Computer Architecture and Computer Engineering, Univeristy of Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034314821","display_name":"Ofer Hadar","orcid":"https://orcid.org/0000-0002-6089-8401"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Ofer Hadar","raw_affiliation_strings":["Ben-Gurion University of the Negev,Israel","Ben-Gurion University of the Negev, Israel"],"affiliations":[{"raw_affiliation_string":"Ben-Gurion University of the Negev,Israel","institution_ids":["https://openalex.org/I124227911"]},{"raw_affiliation_string":"Ben-Gurion University of the Negev, Israel","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059033571","display_name":"Thang Vu","orcid":"https://orcid.org/0000-0003-0486-6349"},"institutions":[{"id":"https://openalex.org/I4210098364","display_name":"Institute for Language and Speech Processing","ror":"https://ror.org/00z24kr14","country_code":"GR","type":"nonprofit","lineage":["https://openalex.org/I4210098364"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Thang Vu","raw_affiliation_strings":["Institute for Natural Language Processing, Univ. of Stutgart,Germany","Institute for Natural Language Processing, Univ. of Stutgart, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Natural Language Processing, Univ. of Stutgart,Germany","institution_ids":["https://openalex.org/I4210098364"]},{"raw_affiliation_string":"Institute for Natural Language Processing, Univ. of Stutgart, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027416202","display_name":"Ilia Polian","orcid":"https://orcid.org/0000-0002-6563-2725"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ilia Polian","raw_affiliation_strings":["Institute of Computer Architecture and Computer Engineering, Univeristy of Stuttgart,Germany","Institute of Computer Architecture and Computer Engineering, Univeristy of Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Architecture and Computer Engineering, Univeristy of Stuttgart,Germany","institution_ids":["https://openalex.org/I100066346"]},{"raw_affiliation_string":"Institute of Computer Architecture and Computer Engineering, Univeristy of Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081626228"],"corresponding_institution_ids":["https://openalex.org/I100066346"],"apc_list":null,"apc_paid":null,"fwci":0.1799,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.50292314,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9962000250816345,"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":0.9962000250816345,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8328613042831421},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.8222154974937439},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6814746856689453},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.5921005606651306},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.576466977596283},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5197027325630188},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5092357397079468},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5000903606414795},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48669034242630005},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.48062843084335327},{"id":"https://openalex.org/keywords/uncorrelated","display_name":"Uncorrelated","score":0.4322810471057892},{"id":"https://openalex.org/keywords/strengths-and-weaknesses","display_name":"Strengths and weaknesses","score":0.4106051027774811},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3425247073173523},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2202456295490265},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10770219564437866},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08574971556663513},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08128389716148376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8328613042831421},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.8222154974937439},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6814746856689453},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.5921005606651306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.576466977596283},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5197027325630188},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5092357397079468},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5000903606414795},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48669034242630005},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.48062843084335327},{"id":"https://openalex.org/C169345407","wikidata":"https://www.wikidata.org/wiki/Q8216221","display_name":"Uncorrelated","level":2,"score":0.4322810471057892},{"id":"https://openalex.org/C63882131","wikidata":"https://www.wikidata.org/wiki/Q17122954","display_name":"Strengths and weaknesses","level":2,"score":0.4106051027774811},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3425247073173523},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2202456295490265},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10770219564437866},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08574971556663513},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08128389716148376},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme52920.2022.9859954","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859954","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"},{"score":0.41999998688697815,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320325850","display_name":"Universit\u00e4t Stuttgart","ror":"https://ror.org/04vnq7t77"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2096733369","https://openalex.org/W2891145043","https://openalex.org/W2911424785","https://openalex.org/W2955425717","https://openalex.org/W2982058372","https://openalex.org/W3019200173","https://openalex.org/W3034900344","https://openalex.org/W3035446294","https://openalex.org/W3088210628","https://openalex.org/W3093010840","https://openalex.org/W3125803510","https://openalex.org/W3127084502","https://openalex.org/W3163660050","https://openalex.org/W3173775919","https://openalex.org/W3173850174","https://openalex.org/W3186436325","https://openalex.org/W4297748527","https://openalex.org/W4394647242","https://openalex.org/W6762718338","https://openalex.org/W6779607597","https://openalex.org/W6783226985","https://openalex.org/W6783682614","https://openalex.org/W6795758371","https://openalex.org/W6864218697"],"related_works":["https://openalex.org/W2381242807","https://openalex.org/W3126131230","https://openalex.org/W2347541121","https://openalex.org/W2080951048","https://openalex.org/W4288804799","https://openalex.org/W3032237421","https://openalex.org/W2390346111","https://openalex.org/W3011883280","https://openalex.org/W2369082698","https://openalex.org/W2401808953"],"abstract_inverted_index":{"Identification":[0],"of":[1,38,44,96],"DeepFake":[2,21,89,151],"video":[3],"content":[4],"is":[5,41],"a":[6,12,78],"challenging":[7],"scientific":[8],"problem":[9,153],"that":[10,46,100,141],"addresses":[11],"growing":[13],"societal":[14],"concern.":[15],"We":[16,76,98],"investigate":[17],"the":[18,42,119,142,150],"relationship":[19],"between":[20],"detection":[22],"by":[23,26,108,115],"humans":[24,107,111],"and":[25,52,71,88,92,146,156,160],"automatic":[27],"methods":[28],"based":[29,63],"on":[30,64,130,136],"state-of-the-art":[31],"deep":[32,102],"learning":[33],"algorithms.":[34],"The":[35],"main":[36],"novelty":[37],"our":[39],"work":[40],"consideration":[43],"videos":[45,90],"are":[47,112,123,148,162],"transmitted":[48],"through":[49],"noisy":[50,68],"channels":[51],"arrive":[53],"with":[54,81,91],"distortions.":[55,97],"This":[56],"reflects":[57],"many":[58],"practical":[59],"environments,":[60],"including":[61],"surveillance":[62],"cameras":[65],"connected":[66],"via":[67],"wireless":[69],"links":[70],"videoconferencing":[72],"in":[73],"driving":[74],"vehicles.":[75],"conduct":[77],"user":[79],"study":[80],"192":[82],"probands":[83],"who":[84],"classify":[85],"real":[86],"(genuine)":[87],"without":[93],"various":[94],"classes":[95],"find":[99],"today's":[101],"neural":[103],"networks":[104],"(DNNs)":[105],"outperform":[106],"far,":[109],"whereas":[110],"heavily":[113],"distracted":[114],"random":[116],"noise":[117],"from":[118],"channel.":[120],"Moreover,":[121],"DNNs":[122,147],"robust":[124],"under":[125],"distortions,":[126],"achieving":[127],"perfect":[128],"classification":[129,152],"distorted":[131],"data":[132],"even":[133],"when":[134],"trained":[135],"distortion-free":[137],"content.":[138],"It":[139],"appears":[140],"human":[143],"visual":[144],"system":[145],"approaching":[149],"quite":[154],"differently":[155],"their":[157],"respective":[158],"strengths":[159],"weaknesses":[161],"largely":[163],"uncorrelated.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
