SVM Classification with Steganography using Video Copy Detection based on Network Security with Machine Learning Framework

SVM Classification with Steganography using Video Copy Detection based on Network Security with Machine Learning Framework | 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 SVM Classification with Steganography using Video Copy Detection based on Network Security with Machine Learning Framework P Karthika, S Balamurali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4777577/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 Its incomplete duplicate detection in the videos problem aimed to identify whether each or many segments of a request video are already present in this study, even while giving data on related fraction time periods. At the moment, the most efficient incomplete copy detection techniques in videos have been designed in 3 components: extraction of features, pattern matching, as well as time integration. To those level, the isolation of the feature identification but also time alignment modules ignores the spatially information of an incomplete copy. In order to decrease the above loss, we begin by representing video frames but also extracting SVM features. As a result, in this paper, we examine the algorithm's performance on the particularly complex video duplicate recognition data-set VCDB, that also, once again, outperforms the government's incomplete copy detection method. Duplicate Video Detection using Steganography Fraction Time Periods SVM classification Query Video VCDB dataset Machine Learning Framework 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-4777577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":340788095,"identity":"af4426b4-da95-4ed2-baff-220c40f55c5a","order_by":0,"name":"P Karthika","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACCSBmZjBIAFGHGR6AxRgbCGlhbIZoYUtmSCBeC1gtjzFUCwEg2X72+eOCgjR5BumezwaJbQzy/A3MbQ/waZHmSTdsnmGQY9ggc3ZzAlCL4YwDjO0G+LTIMaQxNvMYVDA2SORuPgDUwriBgbFNAq8W/mdgLfYNEjmPQVrsCWqRlgDbkpMI1MIMclgiQS2SM54xzuYxSEtukzlmbJBwTiJ5xmECWiTOpzF85vmTbNsv3fxY4kOZjW1/e/szvFrggA2iTAKUGIgFxJk8CkbBKBgFIxEAAOpWQErYviLoAAAAAElFTkSuQmCC","orcid":"","institution":"Kalasalingam Academy of Research and Education /Krishnan koil","correspondingAuthor":true,"prefix":"","firstName":"P","middleName":"","lastName":"Karthika","suffix":""},{"id":340788096,"identity":"6db238c1-a9cb-4616-8930-b240848b6a97","order_by":1,"name":"S Balamurali","email":"","orcid":"","institution":"Kalasalingam Academy of Research and Education /Krishnan koil","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Balamurali","suffix":""}],"badges":[],"createdAt":"2024-07-21 16:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4777577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4777577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74110760,"identity":"ad46b76f-2bab-44d2-acd4-eb80552b0832","added_by":"auto","created_at":"2025-01-17 23:46:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":567308,"visible":true,"origin":"","legend":"","description":"","filename":"SVMClassificationwithSteganography.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4777577/v1_covered_111f8b96-ee22-4eb7-86f8-4c7039d47eb2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SVM Classification with Steganography using Video Copy Detection based on Network Security with Machine Learning Framework","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":"Duplicate Video Detection using Steganography, Fraction Time Periods, SVM classification, Query Video, VCDB dataset, Machine Learning Framework","lastPublishedDoi":"10.21203/rs.3.rs-4777577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4777577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIts incomplete duplicate detection in the videos problem aimed to identify whether each or many segments of a request video are already present in this study, even while giving data on related fraction time periods. 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