{"id":"https://openalex.org/W4415974425","doi":"https://doi.org/10.1016/j.procs.2025.09.529","title":"Cross-Attention-Enhanced Multimodal Fake News Detection using Autoencoder-based Fusion and Transformer-based models","display_name":"Cross-Attention-Enhanced Multimodal Fake News Detection using Autoencoder-based Fusion and Transformer-based models","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415974425","doi":"https://doi.org/10.1016/j.procs.2025.09.529"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2025.09.529","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.529","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2025.09.529","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117312656","display_name":"Rahma Ghorbel","orcid":null},"institutions":[{"id":"https://openalex.org/I142899784","display_name":"University of Sfax","ror":"https://ror.org/04d4sd432","country_code":"TN","type":"education","lineage":["https://openalex.org/I142899784"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Rahma Ghorbel","raw_affiliation_strings":["MIRACL laboratory, University Of Sfax, Tunisia"],"affiliations":[{"raw_affiliation_string":"MIRACL laboratory, University Of Sfax, Tunisia","institution_ids":["https://openalex.org/I142899784"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117312657","display_name":"Hanen Ameur","orcid":null},"institutions":[{"id":"https://openalex.org/I142899784","display_name":"University of Sfax","ror":"https://ror.org/04d4sd432","country_code":"TN","type":"education","lineage":["https://openalex.org/I142899784"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Hanen Ameur","raw_affiliation_strings":["MIRACL laboratory, University Of Sfax, Tunisia"],"affiliations":[{"raw_affiliation_string":"MIRACL laboratory, University Of Sfax, Tunisia","institution_ids":["https://openalex.org/I142899784"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002474870","display_name":"Yassine Ben Ayed","orcid":"https://orcid.org/0000-0002-3676-3670"},"institutions":[{"id":"https://openalex.org/I142899784","display_name":"University of Sfax","ror":"https://ror.org/04d4sd432","country_code":"TN","type":"education","lineage":["https://openalex.org/I142899784"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Yassine Ben Ayed","raw_affiliation_strings":["MIRACL laboratory, University Of Sfax, Tunisia"],"affiliations":[{"raw_affiliation_string":"MIRACL laboratory, University Of Sfax, Tunisia","institution_ids":["https://openalex.org/I142899784"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002474870","https://openalex.org/A5117312656","https://openalex.org/A5117312657"],"corresponding_institution_ids":["https://openalex.org/I142899784"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43712016,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"270","issue":null,"first_page":"4044","last_page":"4053"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9926999807357788,"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"}},"topics":[{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9926999807357788,"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"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.0017000000225380063,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14347","display_name":"Big Data and Digital Economy","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.6802999973297119},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5720999836921692},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49380001425743103},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4733000099658966},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43479999899864197},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4253999888896942},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3968999981880188},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.37630000710487366},{"id":"https://openalex.org/keywords/fake-news","display_name":"Fake news","score":0.37459999322891235}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9082000255584717},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6802999973297119},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6438000202178955},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5720999836921692},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4253999888896942},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C2779756789","wikidata":"https://www.wikidata.org/wiki/Q28549308","display_name":"Fake news","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.37049999833106995},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3628000020980835},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.31940001249313354},{"id":"https://openalex.org/C13672336","wikidata":"https://www.wikidata.org/wiki/Q3460803","display_name":"Bag-of-words model","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.2989000082015991},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2750000059604645},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2025.09.529","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.529","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2025.09.529","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.529","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2909431601","https://openalex.org/W2912305564","https://openalex.org/W3031781733","https://openalex.org/W3159686626","https://openalex.org/W3183364994","https://openalex.org/W3210131246","https://openalex.org/W4206218388","https://openalex.org/W4206390933","https://openalex.org/W4281898186","https://openalex.org/W4382934483","https://openalex.org/W4386172480","https://openalex.org/W4404945594"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,151],"rapid":[2],"spread":[3],"of":[4,36,50,155],"information":[5],"online,":[6],"distinguishing":[7],"between":[8],"real":[9],"and":[10,21,89,111,129,131,148,153],"fake":[11,64,117],"content":[12],"is":[13],"crucial.":[14],"Fake":[15],"news":[16,65,118],"often":[17],"manipulates":[18],"both":[19],"textual":[20,88],"visual":[22,90],"modalities":[23],"to":[24,44,106],"mislead":[25],"readers,":[26],"posing":[27],"significant":[28,142],"challenges":[29],"for":[30,63,115],"detection":[31,66],"systems.":[32],"A":[33],"key":[34],"limitation":[35],"many":[37],"existing":[38,136],"methods":[39],"lies":[40],"in":[41,144],"their":[42],"inability":[43],"effectively":[45],"learn":[46],"a":[47,58,69,74,92,103,113],"unified":[48],"representation":[49],"multimodal":[51,94,158],"data.":[52],"In":[53],"this":[54],"study,":[55],"we":[56],"propose":[57],"new":[59],"neural":[60],"network":[61],"architecture":[62],"that":[67,97],"integrates":[68],"bimodal":[70],"transformer-based":[71],"model":[72,79,123],"with":[73,102],"binary":[75],"classification":[76],"module.":[77],"Our":[78],"comprises":[80],"three":[81],"main":[82],"components:":[83],"(1)":[84],"feature":[85],"extraction":[86],"from":[87],"inputs(2)":[91],"hybrid":[93],"fusion":[95,101],"module":[96],"combines":[98],"autoencoder-based":[99],"early":[100],"cross-attention":[104],"mechanism":[105],"capture":[107],"deep":[108],"inter-modal":[109],"relationships,":[110],"(3)":[112],"classifier":[114],"final":[116],"prediction.":[119],"We":[120],"evaluate":[121],"our":[122,156],"on":[124],"benchmark":[125],"datasets,":[126],"including":[127],"Fakedit":[128],"FakeNewsNet,":[130],"demonstrate":[132],"its":[133],"superiority":[134],"over":[135],"methods.":[137],"The":[138],"experimental":[139],"results":[140],"show":[141],"improvements":[143],"accuracy,":[145],"precision,":[146],"recall,":[147],"F1-score,":[149],"validating":[150],"effectiveness":[152],"robustness":[154],"cross-attention-enhanced":[157],"framework.":[159]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-06T00:00:00"}
