Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11801
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dc.contributor.authorDemirok, B.-
dc.contributor.authorMergen, S.-
dc.contributor.authorOz, B.-
dc.contributor.authorKutlu, M.-
dc.date.accessioned2024-09-22T13:30:58Z-
dc.date.available2024-09-22T13:30:58Z-
dc.date.issued2024-
dc.identifier.issn1613-0073-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11801-
dc.description25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024 -- 9 September 2024 through 12 September 2024 -- Grenoble -- 201493en_US
dc.description.abstractAs we increasingly integrate artificial intelligence into our daily tasks, it is crucial to ensure that these systems are reliable and robust against adversarial attacks. In this paper, we present our participation in Task 6 of CLEF CheckThat! 2024 lab. In our work, we explore several methods, which can be grouped into two categories. The first group focuses on using a genetic algorithm to detect words and changing them via several methods such as adding/deleting words and using homoglyphs. In the second group of methods, we use large language models to generate adversarial attacks. Based on our comprehensive experiments, we pick the genetic algorithm-based model which utilizes a combination of splitting words and homoglyphs as a text manipulation method, as our primary model. We are ranked third based on both BODEGA metric and manual evaluation. © 2024 Copyright for this paper by its authors.en_US
dc.language.isoenen_US
dc.publisherCEUR-WSen_US
dc.relation.ispartofCEUR Workshop Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdversarial Examplesen_US
dc.subjectCredibility Assessmenten_US
dc.subjectNatural Language Processingen_US
dc.subjectRobustnessen_US
dc.subjectGenerative adversarial networksen_US
dc.subjectGenetic algorithmsen_US
dc.subjectCredibility assessmenten_US
dc.subjectDaily tasksen_US
dc.subjectLanguage modelen_US
dc.subjectLanguage processingen_US
dc.subjectNatural language processingen_US
dc.subjectNatural languagesen_US
dc.subjectRobustnessen_US
dc.subjectSecond groupen_US
dc.subjectSplittingsen_US
dc.subjectText manipulationen_US
dc.subjectAdversarial machine learningen_US
dc.titleTurquaz at Checkthat! 2024: Creating Adversarial Examples Using Genetic Algorithmen_US
dc.typeConference Objecten_US
dc.departmentTOBB ETÜen_US
dc.identifier.volume3740en_US
dc.identifier.startpage396en_US
dc.identifier.endpage404en_US
dc.identifier.scopus2-s2.0-85201618529en_US
dc.institutionauthor-
dc.authorscopusid59280903200-
dc.authorscopusid59278835400-
dc.authorscopusid59279864500-
dc.authorscopusid35299304300-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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