Generative Image Steganography Based on Point Cloud

Generative Image Steganography Based on Point Cloud | 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 Generative Image Steganography Based on Point Cloud Yangjie Zhong, Jia Liu, Meiqi Liu, Yan Ke, Minqing Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5388114/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 In deep steganography, the model size is usually related to the underlying mesh resolution, and a separate neural network needs to be trained as a message extractor. In this paper, we propose a generative image steganography based on point cloud representation, which represents image data as a point cloud, learns the distribution of the point cloud data, and represents it in the form of a continuous function. This method breaks through the limitation of the image resolution, and can generate images with arbitrary resolution according to the actual need, and omits the need for explicit data for image steganography. At the same time, using a fixed point cloud extractor transfers the training of the network to the point cloud data, which saves the training time and avoids the risk of exposing the steganography behavior caused by the transmission of the message extractor. Experiments prove that the steganographic images generated by the scheme have very high image quality and the accuracy of message extraction reaches more than 99%. Information Hiding Steganography Implicit Neural Representation Point Cloud 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-5388114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374466788,"identity":"5edea56b-a1e9-496c-b385-02b439344de1","order_by":0,"name":"Yangjie Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie3RsQrCMBCA4ZRAuhy6nrTUJxACglAQ+wI+xJWu7e4YEOro6uBbCJ0jglMfoKMuTl2cpItaN50aN8H8+8fdcYzZbD+YcJf7U3x/QJ9zbUZ6cEzkSXB/sMrJjAQ4nwxaMpVlKQ0XQyaRQACr0mtVs1kwUl3EUyQJAZxNtgu3LBlPdBfxtSaSCByzwgOm46KTYKxeBgSmF1OSOIo0AUApDAkcOWsHAbr5ONxKg1uGq/WtaZSOogM/V/ViFnSSjxAMX/NOvhU2m832Fz0B3NY+aaI8EhQAAAAASUVORK5CYII=","orcid":"","institution":"Engineering University of PAP","correspondingAuthor":true,"prefix":"","firstName":"Yangjie","middleName":"","lastName":"Zhong","suffix":""},{"id":374466789,"identity":"ee43c928-fc2f-4807-8b1f-b0d1033784f2","order_by":1,"name":"Jia Liu","email":"","orcid":"","institution":"Engineering University of PAP","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":374466790,"identity":"261a60fc-722d-47c8-ba0a-b5e60b4fd8e8","order_by":2,"name":"Meiqi Liu","email":"","orcid":"","institution":"Engineering University of PAP","correspondingAuthor":false,"prefix":"","firstName":"Meiqi","middleName":"","lastName":"Liu","suffix":""},{"id":374466791,"identity":"07e25eea-ba57-4422-956b-782ab055b557","order_by":3,"name":"Yan Ke","email":"","orcid":"","institution":"Engineering University of PAP","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Ke","suffix":""},{"id":374466792,"identity":"c1a4a3a8-bfc7-49bd-b394-0e4bf8548394","order_by":4,"name":"Minqing Zhang","email":"","orcid":"","institution":"Engineering University of PAP","correspondingAuthor":false,"prefix":"","firstName":"Minqing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-04 12:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5388114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5388114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72688510,"identity":"1fff5733-0bbb-4e9d-9011-a66f6fd54ec1","added_by":"auto","created_at":"2024-12-31 08:54:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1108645,"visible":true,"origin":"","legend":"","description":"","filename":"MS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5388114/v1_covered_d8f5e010-0051-4a93-baf1-e7986b030d2d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generative Image Steganography Based on Point Cloud","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":"Information Hiding, Steganography, Implicit Neural Representation, Point Cloud","lastPublishedDoi":"10.21203/rs.3.rs-5388114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5388114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn deep steganography, the model size is usually related to the underlying mesh resolution, and a separate neural network needs to be trained as a message extractor. In this paper, we propose a generative image steganography based on point cloud representation, which represents image data as a point cloud, learns the distribution of the point cloud data, and represents it in the form of a continuous function. This method breaks through the limitation of the image resolution, and can generate images with arbitrary resolution according to the actual need, and omits the need for explicit data for image steganography. At the same time, using a fixed point cloud extractor transfers the training of the network to the point cloud data, which saves the training time and avoids the risk of exposing the steganography behavior caused by the transmission of the message extractor. 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