Enhancing Fake Image Detection with Ensembled Convolutional Neural Networks

Enhancing Fake Image Detection with Ensembled Convolutional Neural Networks | 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 Enhancing Fake Image Detection with Ensembled Convolutional Neural Networks Adeeb Khan, Sarsij Tripathi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6629905/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 Fake image detection has emerged as a vital task for the Generative AI era due to the fast evolution in generations of models that have made highly realistic synthetic images possible. In this paper, we formulate an ensemble-based Convolutional Neural Network (CNN) to enhance fake image detection accuracy. Our methodology includes the training of five CNN models on separate datasets consisting of real and artificially created images found in different public datasets. The artificially created images in the datasets are produced using the latest models that include StyleGAN2, StyleGAN3, Diffusion GAN, Taming Transformer and Gansformer. The outputs of the five CNN models are fused using a stacking ensemble process in which several different classifiers such as Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine, Multi-Layer Perceptron and Logistic Regression are utilized to boost the final classification performance. The ultimate test on unseen data reveals an increase in the classification performance as our approach exhibits a high accuracy rate of more than 90%. Comparison of the performance of different classifiers utilized in the stacking ensemble and accuracy metrics such as precision, recall and F1-score reveals a complete insight about the performance of the proposed approach. These results indicate that the use of ensemble-based deep learning approaches makes fake image detection systems strongly robust in nature and even more applicable in real-world settings. Fake Image Detection Convolutional Neural Networks Ensemble Learning Machine Learning ConvNext 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. 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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-6629905","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455419494,"identity":"8e8354da-fe48-435f-9ae9-ce1e563e0789","order_by":0,"name":"Adeeb Khan","email":"data:image/png;base64,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","orcid":"","institution":"Motilal Nehru National Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Adeeb","middleName":"","lastName":"Khan","suffix":""},{"id":455419496,"identity":"f91cc9b0-9299-4615-ab6c-f4f93e73dc9b","order_by":1,"name":"Sarsij Tripathi","email":"","orcid":"","institution":"Motilal Nehru National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sarsij","middleName":"","lastName":"Tripathi","suffix":""}],"badges":[],"createdAt":"2025-05-09 15:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6629905/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6629905/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82565775,"identity":"4c5e563a-e3f5-43dd-b536-05c13f49e8fb","added_by":"auto","created_at":"2025-05-13 02:46:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1658507,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancingFakeImageDetectionWithEnsembledConvolutionalNeuralNetworks.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6629905/v1_covered_8b18240c-c153-4862-80bb-3fce7147545d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Fake Image Detection with Ensembled Convolutional Neural Networks","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":"Fake Image Detection, Convolutional Neural Networks, Ensemble Learning, Machine Learning, ConvNext","lastPublishedDoi":"10.21203/rs.3.rs-6629905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6629905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFake image detection has emerged as a vital task for the Generative AI era due to the fast evolution in generations of models that have made highly realistic synthetic images possible. 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