Enhancing Data Hiding Techniques in Image Processing through AI-Driven Edge Computing

Enhancing Data Hiding Techniques in Image Processing through AI-Driven Edge Computing | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 May 2025 V1 Latest version Share on Enhancing Data Hiding Techniques in Image Processing through AI-Driven Edge Computing Author : Zahid Hussain 0009-0002-3407-5783 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174837867.72873213/v1 244 views 112 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In the digital era, data security and confidentiality are of paramount importance, particularly in image-based communications. This study explores the integration of Artificial Intelligence (AI) with edge computing to enhance data hiding techniques in image processing. Traditional methods of steganography often face challenges related to detection resistance, data capacity, and computational efficiency. By leveraging the localized processing capabilities of edge computing and the adaptive learning features of AI, this research proposes a hybrid model that ensures real-time, secure, and intelligent data embedding. The model employs deep learning algorithms for identifying optimal embedding regions within images based on texture complexity and perceptual invisibility, thereby maximizing data payload while maintaining visual integrity. Additionally, edge devices are used to process and embed data at the source, significantly reducing latency and exposure to cyber threats. Experimental results demonstrate improved robustness against stainless attacks, enhanced embedding efficiency, and greater adaptability to dynamic image conditions. This approach not only bolsters data security but also aligns with the growing demand for decentralized and privacy-preserving computing in Internet of Things (IoT) and multimedia applications. Supplementary Material File (1.pdf) Download 124.28 KB Information & Authors Information Version history V1 Version 1 27 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ai cybersecurity data hiding deep learning edge computing image processing iot steganography Authors Affiliations Zahid Hussain 0009-0002-3407-5783 [email protected] University of Lahore View all articles by this author Metrics & Citations Metrics Article Usage 244 views 112 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zahid Hussain. Enhancing Data Hiding Techniques in Image Processing through AI-Driven Edge Computing. Authorea . 27 May 2025. DOI: https://doi.org/10.22541/au.174837867.72873213/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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