{"id":"https://openalex.org/W4414170424","doi":"https://doi.org/10.1109/avss65446.2025.11149951","title":"Position-Aware CNN-Transformer Model for Deepfake Detection","display_name":"Position-Aware CNN-Transformer Model for Deepfake Detection","publication_year":2025,"publication_date":"2025-08-11","ids":{"openalex":"https://openalex.org/W4414170424","doi":"https://doi.org/10.1109/avss65446.2025.11149951"},"language":"en","primary_location":{"id":"doi:10.1109/avss65446.2025.11149951","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss65446.2025.11149951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Advanced Visual and Signal-Based Systems (AVSS)","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/A5034641497","display_name":"Oscal Tzyh-Chiang Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I148099254","display_name":"National Chung Cheng University","ror":"https://ror.org/0028v3876","country_code":"TW","type":"education","lineage":["https://openalex.org/I148099254"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Oscal Tzyh-Chiang Chen","raw_affiliation_strings":["National Chung Cheng University,Department of Electrical Engineering,Chaiyi,Taiwan"],"affiliations":[{"raw_affiliation_string":"National Chung Cheng University,Department of Electrical Engineering,Chaiyi,Taiwan","institution_ids":["https://openalex.org/I148099254"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057812257","display_name":"Chih-Ta Tsai","orcid":"https://orcid.org/0000-0002-3841-9355"},"institutions":[{"id":"https://openalex.org/I148099254","display_name":"National Chung Cheng University","ror":"https://ror.org/0028v3876","country_code":"TW","type":"education","lineage":["https://openalex.org/I148099254"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cheng Tai Tsai","raw_affiliation_strings":["National Chung Cheng University,Department of Electrical Engineering,Chaiyi,Taiwan"],"affiliations":[{"raw_affiliation_string":"National Chung Cheng University,Department of Electrical Engineering,Chaiyi,Taiwan","institution_ids":["https://openalex.org/I148099254"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043825355","display_name":"Yu Xin Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I148099254","display_name":"National Chung Cheng University","ror":"https://ror.org/0028v3876","country_code":"TW","type":"education","lineage":["https://openalex.org/I148099254"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu Xin Chen","raw_affiliation_strings":["National Chung Cheng University,Department of Electrical Engineering,Chaiyi,Taiwan"],"affiliations":[{"raw_affiliation_string":"National Chung Cheng University,Department of Electrical Engineering,Chaiyi,Taiwan","institution_ids":["https://openalex.org/I148099254"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034641497"],"corresponding_institution_ids":["https://openalex.org/I148099254"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23266065,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9994999766349792,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9950000047683716,"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/robustness","display_name":"Robustness (evolution)","score":0.7427999973297119},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5856000185012817},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4884999990463257},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.47600001096725464},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.45989999175071716},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.45730000734329224},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4562999904155731},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42719998955726624}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7427999973297119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7314000129699707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7264000177383423},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5856000185012817},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.45730000734329224},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4562999904155731},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4056999981403351},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.30630001425743103},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26589998602867126},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/avss65446.2025.11149951","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss65446.2025.11149951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Advanced Visual and Signal-Based Systems (AVSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320331164","display_name":"National Science and Technology Council","ror":"https://ror.org/00wnb9798"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2891145043","https://openalex.org/W2904573504","https://openalex.org/W2962770929","https://openalex.org/W2963720850","https://openalex.org/W3036167779","https://openalex.org/W3094728142","https://openalex.org/W3108228772","https://openalex.org/W3158353280","https://openalex.org/W4292829111","https://openalex.org/W4294643831","https://openalex.org/W4298289240","https://openalex.org/W4312453532","https://openalex.org/W4320036918","https://openalex.org/W4383472654","https://openalex.org/W4392910325","https://openalex.org/W4402595072","https://openalex.org/W4402916453"],"related_works":[],"abstract_inverted_index":{"This":[0,67],"study":[1],"tackles":[2],"the":[3,70,158],"challenges":[4],"of":[5,91,148],"Deepfake":[6],"detection":[7,32],"posed":[8],"by":[9,27,135,171],"modern":[10],"generative":[11,101],"models":[12,102],"and":[13,55,76,82,108,113,120,141,152,165],"introduces":[14],"an":[15],"integrated":[16],"architecture":[17],"that":[18,125],"combines":[19],"Convolutional":[20],"Neural":[21],"Network":[22],"(CNN)":[23],"with":[24,59],"Transformer,":[25],"enhanced":[26],"position-aware":[28,50,64],"modules":[29],"to":[30,74],"improve":[31],"accuracy.":[33],"While":[34],"Transformer":[35],"inherently":[36],"incorporates":[37],"positional":[38,60],"encoding,":[39],"CNN":[40],"lacks":[41],"explicit":[42],"spatial":[43,78],"modeling":[44],"capabilities.":[45],"To":[46,80],"address":[47],"this,":[48],"our":[49,84,126],"mechanism":[51],"fuses":[52],"RGB":[53],"images":[54,97,116,133],"CNN-derived":[56],"feature":[57],"maps":[58],"encoding":[61],"information":[62],"using":[63],"attention":[65],"modules.":[66],"integration":[68],"strengthens":[69],"backbone":[71],"network\u2019s":[72],"ability":[73],"capture":[75],"interpret":[77],"cues.":[79],"train":[81],"evaluate":[83],"model,":[85],"we":[86],"constructed":[87],"a":[88],"comprehensive":[89],"dataset":[90],"80,000":[92],"facial":[93],"images\u2014comprising":[94],"40,000":[95,114],"AI-generated":[96],"from":[98,118],"four":[99],"cutting-edge":[100],"(StyleGAN3,":[103],"EG3D,":[104,137],"Stable":[105,138],"Diffusion":[106,139],"XL,":[107,140,144],"DALL-E":[109,142],"3":[110,143],"XL":[111],"v2)":[112],"real":[115],"sourced":[117],"CelebA-HQ":[119],"FFHQ.":[121],"Experimental":[122],"results":[123],"show":[124],"model":[127],"consistently":[128],"outperforms":[129],"baseline":[130],"approaches":[131],"on":[132],"generated":[134,170],"StyleGAN3,":[136],"achieving":[145],"accuracy":[146],"gains":[147],"8.49%,":[149],"2.93%,":[150],"2.40%,":[151],"4.40%,":[153],"respectively.":[154],"These":[155],"findings":[156],"highlight":[157],"model\u2019s":[159],"superior":[160],"performance,":[161],"strong":[162],"generalization":[163],"capability,":[164],"robustness":[166],"in":[167],"detecting":[168],"Deepfakes":[169],"state-of-the-art":[172],"AI":[173],"models.":[174]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}