GISNet: Lightweight image-to-image steganography based on improved emd and dual-domain graph convolutional network

GISNet: Lightweight image-to-image steganography based on improved emd and dual-domain graph convolutional network | 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 GISNet: Lightweight image-to-image steganography based on improved emd and dual-domain graph convolutional network Xintao Duan, Rusheng Chen, Sen Li, Chuan Qin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9223895/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Image Steganography Technology embeds secret images into cover images to protect privacy without raising suspicion from third parties. However, methods limited to single-domain processing struggle to simultaneously achieve concealment, security, and efficiency, while high-performance models are often too complex for lightweight deployment. To address these issues, this paper proposes a lightweight image steganography network model named GISNet, based on improved Empirical Mode Decomposition and dual-domain graph convolution. The model employs an enhanced Empirical Mode Decomposition module to adaptively decompose the secret image into multiple scales and utilizes multi-scale spatial-frequency blocks along with graph convolution modules to achieve deep integration and collaborative optimization of spatial and frequency domain features. The experimental results demonstrate that GISNet outperforms existing mainstream steganographic models. On the DIV2K dataset, the Peak Signal-to-Noise Ratio (PSNR) of cover/steganographic images and secret/recovered images increases by 1.77 dB and 4.65 dB, respectively. Moreover, GISNet exhibits notable lightweight characteristics, with only 8.00M parameters, 7.88 GFLOPs of computational cost, and an inference time of 76 ms, which demonstrates its high efficiency and suitability for resource-constrained environments. Image steganography empirical mode decomposition dual-domain model graph convolutional network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 25 Mar, 2026 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-9223895","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622335960,"identity":"4b01bd76-c9bd-4be0-9578-7ed426a7dc68","order_by":0,"name":"Xintao Duan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDCCAyDCAEQwH0MSIUKLBAMDWxqEQ5wWBpAWHjPitPDdSH72mKfgTh1/e8+3xx/bGOT4biQwfi7Ao0XyRpq54QyDZxISZ85uNzjYxmAseSOBWXoGHi0GNxLMJD4YHJZguJG7TQKoJXHDjQQ2Zh68WtK/SSQAtcjfyHkG0lJPhJYciC1ABhtIS4IBIS2SZ96USc4wOCy58cwxM4kz5yQMZ5552CyNTwvf8fRt0jx/DvPLHW9+JlFRZiPPdzz54Gd8WtABMH4YGBtI0DAKRsEoGAWjABsAAJGuUD8B7gLsAAAAAElFTkSuQmCC","orcid":"","institution":"Henan Normal University","correspondingAuthor":true,"prefix":"","firstName":"Xintao","middleName":"","lastName":"Duan","suffix":""},{"id":622335961,"identity":"24ebc5ee-e1f5-4b83-9b71-b3dab16db7ee","order_by":1,"name":"Rusheng Chen","email":"","orcid":"","institution":"Henan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Rusheng","middleName":"","lastName":"Chen","suffix":""},{"id":622335962,"identity":"a9ce7b92-7563-457b-927b-2fbe3cd1aa6b","order_by":2,"name":"Sen Li","email":"","orcid":"","institution":"Henan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Sen","middleName":"","lastName":"Li","suffix":""},{"id":622335963,"identity":"d2f4fd3f-52c7-4581-b038-8e33ead5d946","order_by":3,"name":"Chuan Qin","email":"","orcid":"","institution":"University of Shanghai for Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2026-03-25 13:41:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9223895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9223895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106961847,"identity":"d2440466-7179-462c-9cce-05f33668e079","added_by":"auto","created_at":"2026-04-15 09:27:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4322832,"visible":true,"origin":"","legend":"","description":"","filename":"anonymisedmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9223895/v1_covered_5dad8679-b468-4589-ac6f-d57632286b17.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GISNet: Lightweight image-to-image steganography based on improved emd and dual-domain graph convolutional network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-king-saud-university-computer-and-information-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University Computer and Information Sciences](https://link.springer.com/journal/44443)","snPcode":"44443","submissionUrl":"https://submission.springernature.com/new-submission/44443/3","title":"Journal of King Saud University Computer and Information Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Image steganography, empirical mode decomposition, dual-domain model, graph convolutional network","lastPublishedDoi":"10.21203/rs.3.rs-9223895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9223895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Image Steganography Technology embeds secret images into cover images to protect privacy without raising suspicion from third parties. 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