UAM-Net: Unified Attention EfficientNet for Robust Deepfake Detection

UAM-Net: Unified Attention EfficientNet for Robust Deepfake Detection | 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 UAM-Net: Unified Attention EfficientNet for Robust Deepfake Detection Kerenalli Sudarshana, Yendapalli Vamsidhar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4728068/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 widespread usage of deepfake technology in the rapidly growing area of digital media poses an imminent threat to the authenticity and truthfulness of multimedia content. Deep learning techniques have created hyper-realistic altered visuals, which have caused severe issues in several domains, like social media, politics, and entertainment. This problem necessitates the development of effective deepfake detection tools. Present-day deepfake detection methods rely heavily on Convolutional Neural Networks (CNNs) and associated deep learning architectures. Although these methods have been helpful, they usually fail to capture relational and contextual information within images fully. Their ability to recognize subtle variations typical of sophisticated deepfakes is hindered by it. This paper presents a novel deep learning framework called Unified Attention Mechanism into EfficientNet model (UAM-Net). It integrates channel and spatial attention processes inside the EfficientNet architecture. UAM-Net concentrates on channel and spatial information to increase classification accuracy and feature extraction. UAM-Net performs better than current state-of-the-art models in DFDC-Preview Dataset assessments. UAM-Net achieved an AUC-ROC of 99.81%, recall of 98.95%, accuracy of 97.91%, precision of 96.92%, and F1 score of 97.93%. These results reveal how effectively the model performs in various circumstances and highlight its remarkable ability to differentiate between real and fake data. In addition, UAM-Net takes advantage of Class Activation Mapping (CAM). The CAM provides useful insights into the model's decision-making process and enhances its interpretability and application reliability. Attention Classification Convolutional Neural Networks Deepfakes Feature Extraction Manipulations 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-4728068","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334139540,"identity":"e0a25808-576b-450d-9a6d-61e392caccfb","order_by":0,"name":"Kerenalli Sudarshana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACCShO4GdgbAAJ8BCvRbKBsbGBNC0GB6DWEASS/WcMb/zcU5dnfH5x+4OfOQwy5oS0SEvkGFv2PDtcbHbjYWNj7zYGHktCdslJ8JhJ8Bw4kLjtxsHGBl6gFqALCWjhP2Mm+edAXeLmGQcbG/8So0WaIcdMmucAc+IG/sbGZqJskZyRVmwtc+Bw4owbjI2zZbdJENYicf7wxptvgA7r7z/+4OPbbTb2BLUgaU4Ak0SrBwJ+4k0fBaNgFIyCEQYAn3ZFRTPjEV0AAAAASUVORK5CYII=","orcid":"","institution":"GITAM University","correspondingAuthor":true,"prefix":"","firstName":"Kerenalli","middleName":"","lastName":"Sudarshana","suffix":""},{"id":334139541,"identity":"e9fad484-7b15-4c1c-9f52-825e2c92627c","order_by":1,"name":"Yendapalli Vamsidhar","email":"","orcid":"","institution":"GITAM University","correspondingAuthor":false,"prefix":"","firstName":"Yendapalli","middleName":"","lastName":"Vamsidhar","suffix":""}],"badges":[],"createdAt":"2024-07-12 05:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4728068/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4728068/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62313172,"identity":"1a1a000c-7724-41b9-8b99-5e37033074f4","added_by":"auto","created_at":"2024-08-12 21:46:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":627463,"visible":true,"origin":"","legend":"","description":"","filename":"skPaperSNCompRNPFinalv4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4728068/v1_covered_1badd57f-24b3-4ee2-8990-1ffc235fce36.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"UAM-Net: Unified Attention EfficientNet for Robust Deepfake Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Attention, Classification, Convolutional Neural Networks, Deepfakes, Feature Extraction, Manipulations","lastPublishedDoi":"10.21203/rs.3.rs-4728068/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4728068/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe widespread usage of deepfake technology in the rapidly growing area of digital media poses an imminent threat to the authenticity and truthfulness of multimedia content. 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