A Novel Approach to Image Steganography: A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding

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This paper proposes an image steganography method that fuses integer wavelet transform (IWT) with logistic-map–based chaotic pseudo-random sequences to select image positions for embedding confidential message bits. A genetic algorithm is then used to optimize the embedding by searching for an optimal chromosome representation, aiming to increase capacity while reducing distortion, and the authors evaluate the method against statistical attacks and report high-fidelity metrics (PSNR 55.3392 dB, MSE 0.3620, SSIM 0.9744, VIF 0.9269) and an estimated hidden capacity of 3.67 bits per pixel. The key caveat explicitly stated is that this is a Research Square preprint that has not been peer reviewed by a journal. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Image steganography comprises concealing sensitive data inside digital images to ensure secure communication. In this work, a ground-breaking method for image steganography is introduced, which fuses chaotic and genetic algorithms, aiming to create a strong and impenetrable data concealment technique. The study applies Integer Wavelet Transform (IWT) to the image and services logistic map chaotic system to produce pseudo-random sequences. These sequences take a significant in identifying the most secure positions within the image to embed confidential message bits. This embedding process is highly secure and resilient against statistical attacks. Moreover, a genetic algorithm optimizes this process, bolstering the security of the steganography approach. Using a population-based search technique, the genetic algorithm efficiently explores the search space, identifying the optimal chromosome representation for data embedding. This significantly enhances the embedding capacity while minimizing distortion to the original image. Through extensive experimental evaluations and performance analyses, the study showcases the effectiveness and resilience of this proposed approach. The findings highlight that combining chaotic and genetic algorithms in image steganography markedly improves security, capacity, and resistance to attacks when compared to traditional methods. This steganographic scheme achieved a PSNR of 55.3392 dB, a MSE of 0.3620, a SSIM of 0.9744, and a VIF of 0.9269. Additionally, the work demonstrated a hidden information capacity of 3.67 bits per pixel (BPP).
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A Novel Approach to Image Steganography: A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding | 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 A Novel Approach to Image Steganography: A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding Dineshkumar Thangaraju, Asokan R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4177442/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 Image steganography comprises concealing sensitive data inside digital images to ensure secure communication. In this work, a ground-breaking method for image steganography is introduced, which fuses chaotic and genetic algorithms, aiming to create a strong and impenetrable data concealment technique. The study applies Integer Wavelet Transform (IWT) to the image and services logistic map chaotic system to produce pseudo-random sequences. These sequences take a significant in identifying the most secure positions within the image to embed confidential message bits. This embedding process is highly secure and resilient against statistical attacks. Moreover, a genetic algorithm optimizes this process, bolstering the security of the steganography approach. Using a population-based search technique, the genetic algorithm efficiently explores the search space, identifying the optimal chromosome representation for data embedding. This significantly enhances the embedding capacity while minimizing distortion to the original image. Through extensive experimental evaluations and performance analyses, the study showcases the effectiveness and resilience of this proposed approach. The findings highlight that combining chaotic and genetic algorithms in image steganography markedly improves security, capacity, and resistance to attacks when compared to traditional methods. This steganographic scheme achieved a PSNR of 55.3392 dB, a MSE of 0.3620, a SSIM of 0.9744, and a VIF of 0.9269. Additionally, the work demonstrated a hidden information capacity of 3.67 bits per pixel (BPP). Image Steganography IWT Chaotic Algorithm Genetic Algorithm Logistic map 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. 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