Evaluating Gabor, Latent, and Fused Features for Zero-Shot DeepfakeDetection with Isolation Forest and OCSVM.A comparative Study

Evaluating Gabor, Latent, and Fused Features for Zero-Shot DeepfakeDetection with Isolation Forest and OCSVM.A comparative Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Gabor, Latent, and Fused Features for Zero-Shot DeepfakeDetection with Isolation Forest and OCSVM.A comparative Study B N Jyothi, M A Jabbar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5678475/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The prevalence of deepfake technology has led to increased risks to biometric security, social media integrity, and the useof audio and video content for disinformation. In response, studies have progressed to provide efficient detecting techniques.Specifically, this paper uses anomaly-based classifiers to give a comparative analysis of zero-shot learning-based deepfakedetection. We evaluate two classifiers: One-Class Support Vector Machine (OCSVM) and Isolation Forest (IF) with threedifferent feature settings: Gabor features, Latent features, and Fused features (a mix of Gabor and Latent features). Importantmeasures like F1 Score, Accuracy, Precision, and Recall are used to assess how well the classifiers perform. Our resultsprovide important insights and future directions into the relationship between feature types and classifier performance in thesetting of zero-shot learning. Zero shot learning Deep Fake Detection Gabor Features Latent features Feature Fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5678475","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":408832375,"identity":"3c564a6f-666e-4ccd-966d-2641eaff8b8b","order_by":0,"name":"B N Jyothi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACxgYGBmYGAyCWP9j+4QNQhI2dsBbGZrAWCeY2xhkgLcxEWNQMpiTY25h5QAxCWphnJB9/XFDAwM4v3dj22ObXNnk+ZgbGDx9z8FgxIy2xeQbQYZJzDrYb5/bdNmxjBnJmbsOnJcewmQeoxeBAYoN0bs9tRqAWNmZeorVY9ty2J0HLjcQ2aYYftxMJa+l5ljibx0CCWbLnYLNhb8Pt5DZmxma8fjFsTz7wmeePTTI/e/vDBz/+3Lad39588MNHfFomJIAoiWSInW1gsgG3eiCQ5z8Apu0g3D94FY+CUTAKRsEIBQArZU2lL3GK7QAAAABJRU5ErkJggg==","orcid":"","institution":"vardhaman college of engineering","correspondingAuthor":true,"prefix":"","firstName":"B","middleName":"N","lastName":"Jyothi","suffix":""},{"id":408832376,"identity":"72d293ff-7b9c-4bd4-8bc6-4fe9f2ffb6b2","order_by":1,"name":"M A Jabbar","email":"","orcid":"","institution":"vardhaman college of engineering","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"A","lastName":"Jabbar","suffix":""}],"badges":[],"createdAt":"2024-12-19 16:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5678475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5678475/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75135134,"identity":"12a48ef6-6c05-4663-812a-d224da67750e","added_by":"auto","created_at":"2025-01-31 02:25:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":443904,"visible":true,"origin":"","legend":"","description":"","filename":"ensemblefeatureextractionpaper3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678475/v1_covered_708429c6-c198-4be2-acd4-487239905a28.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Gabor, Latent, and Fused Features for Zero-Shot DeepfakeDetection with Isolation Forest and OCSVM.A comparative Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Zero shot learning, Deep Fake Detection, Gabor Features ,Latent features, Feature Fusion","lastPublishedDoi":"10.21203/rs.3.rs-5678475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5678475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The prevalence of deepfake technology has led to increased risks to biometric security, social media integrity, and the useof audio and video content for disinformation. 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