Implicit Neural Representation Steganography by Neuron Pruning

Implicit Neural Representation Steganography by Neuron Pruning | 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 Implicit Neural Representation Steganography by Neuron Pruning Weina Dong, Jia Liu, Lifeng Chen, Wenquan Sun, Xiaozhong Pan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4417487/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Sep, 2024 Read the published version in Multimedia Systems → Version 1 posted 14 You are reading this latest preprint version Abstract Recently, implicit neural representation (INR) has started to be applied in image steganography. However, the quality of stego and secret images represented by INR is generally low. In this paper, we propose an implicit neural representation steganography method by neuron pruning. Initially, we randomly deactivate a portion of neurons to train an INR function for implicitly representing the secret image. Subsequently, we prune the neurons that are deemed unimportant for representing the secret image in a unstructured manner to obtain a secret function, while marking the positions of neurons as the key. Finally, based on a partial optimization strategy, we reactivate the pruned neurons to construct a stego function for representing the cover image. The recipient only needs the shared key to recover the secret function from the stego function in order to reconstruct the secret image. Experimental results demonstrate that this method not only allows for lossless recovery of the secret image, but also performs well in terms of capacity, fidelity, and undetectability. The experiments conducted on images of different resolutions validate that our proposed method exhibits significant advantages in image quality over existing implicit representation steganography methods. Information Hiding Steganography Implicit Neural Representation Continuous Function Implicit Representation Steganography Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Sep, 2024 Read the published version in Multimedia Systems → Version 1 posted Editorial decision: Revision requested 17 Jul, 2024 Reviews received at journal 27 Jun, 2024 Reviewers agreed at journal 25 Jun, 2024 Reviewers agreed at journal 23 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 05 Jun, 2024 Reviewers invited by journal 05 Jun, 2024 Editor assigned by journal 02 Jun, 2024 Submission checks completed at journal 16 May, 2024 First submitted to journal 14 May, 2024 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. 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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-4417487","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":303099608,"identity":"4edebf88-184f-46e5-ae48-20755a9b24d1","order_by":0,"name":"Weina Dong","email":"","orcid":"","institution":"Engineering University of PAP","correspondingAuthor":false,"prefix":"","firstName":"Weina","middleName":"","lastName":"Dong","suffix":""},{"id":303099609,"identity":"5ba0ac00-4522-4165-84fe-23f896423165","order_by":1,"name":"Jia 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