A Robust Hybrid Framework for Image Security using Chaotic Maps, Steganography, and Convolutional Neural Networks

A Robust Hybrid Framework for Image Security using Chaotic Maps, Steganography, and Convolutional Neural Networks | 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 Article A Robust Hybrid Framework for Image Security using Chaotic Maps, Steganography, and Convolutional Neural Networks Ahmed Makram, Abeer Saber, Wael A. Awad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8187480/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 Safeguarding sensitive visual data against unauthorized access and disclosure is critical, making robust image encryption a paramount necessity for ensuring privacy. This paper presents a novel hybrid framework for secure image coding that significantly enhances both security and privacy by integrating chaotic map-based encryption with steganography. The proposed system utilizes a sophisticated two-phase data concealment and encryption process. Initially, the sensitive image is covertly embedded within a designated cover image using the Discrete Cosine Transform (DCT) domain to achieve high imperceptibility. The resulting transformed image is then subjected to a robust encryption mechanism. This mechanism features a dynamically generated chaotic map, which is constructed by integrating multiple chaotic systems, specifically leveraging a cosine-square logistic map to produce a variable key for initiating new chaotic sequences. These highly randomized sequences are subsequently applied to encrypt the DCT coefficients of the hidden image. Furthermore, to optimize the quality and fidelity of the decrypted image, Convolutional Neural Networks (CNNs), employing diverse filter sets, are incorporated into the decryption pipeline. The efficacy of this new approach is validated through comprehensive numerical experiments conducted on standard gray-scale images. Comparative analysis against established techniques, including circular mapping, S-box implementations, and the S-box combined with the Arnold Transform, demonstrates the superior performance of our methodology. The results confirm that the suggested hybrid scheme achieves higher security metrics, including lower correlation coefficients, and excellent information entropy, significantly outperforming existing image security solutions. Physical sciences/Engineering Physical sciences/Mathematics and computing Image Encryption-Decryption Skew tent map Piecewise Linear Chaotic Map CNNs median Filter gaussian Filter Chosen-plaintext attack Security analysis 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-8187480","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":554459669,"identity":"b8e6f2c6-048d-4c89-99af-b52b9f5fb6b6","order_by":0,"name":"Ahmed Makram","email":"","orcid":"","institution":"Military Academy Air Defense College","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Makram","suffix":""},{"id":554459670,"identity":"d699255e-2aa3-4a7d-a4b5-1263a1550d77","order_by":1,"name":"Abeer Saber","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACNgkow4CBgfEBD4h1gIAWfiQtzAZEaZGcgdDCJkGUFoPbzQcfVzDUyZuz9z6reNvGIMd3I4Hx4Rd8Wu4cSzY8w3DYcGfPcbObc9sYjCVvJDAby+DTciPHTLKB4QDjhhtpbLd52xgSN9xIYJOWwKPF/kb+958NDHX2G+4/YysGaqkHamH/jU8L0BY2xgYGZqDhbGzMQC0JBkBbGD/g94sx0GGHk3f2pDFLzjknYTjzzMNmaTw6QCH28CPQYbbb2Y8xfnhTZiPPdzz54Mcf+PSAAOM/OBPkCcYGZh5CWjDNIGjLKBgFo2AUjCQAAPflT8oI9KWKAAAAAElFTkSuQmCC","orcid":"","institution":"Damietta University","correspondingAuthor":true,"prefix":"","firstName":"Abeer","middleName":"","lastName":"Saber","suffix":""},{"id":554459671,"identity":"26d2406d-588f-402e-a417-d464cf2cccbe","order_by":2,"name":"Wael A. 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