2026· Journal of Cyber Security and Risk Auditing· 0 citations
TL;DR
A new efficient technique for hiding a gray image inside a blue layer of color image is presented by combining the genetic algorithm (GA) with the Least Significant Bit (LSB) approach to optimal solution permutation for embedding pixel assortment of the image where data is to be concealed.
Abstract
Recently, in the science of data hiding, most research papers propose several techniques to conceal data inside the images and ensure secure mediums such as text, images, audio, and videos with preserving its quality. Researchers have been interested in the last decade of these techniques in image steganography and cryptography, this research proposed a system that uses multiple layers of security in which steganography and cryptography are together to enhance security. This study aims to present a new efficient technique for hiding a gray image inside a blue layer of color image by combining the genetic algorithm (GA) with the Least Significant Bit (LSB) approach to optimal solution permutation for embedding pixel assortment of the image where data is to be concealed. This technique offers immovability differential attacks that are evaluated by several performance metrics. According to experimental findings, the stego and the cover image are visually indistinguishable. PSNR, MSE, and SSIM are used as measurement matrices. Our aim passed a successful test of robustness against experimental analysis.
The increasing demand for secure communication over public networks has intensified research on information
hiding techniques capable of protecting sensitive information from unauthorized access. Image steganography achieves this
objective by concealing secret information within digital images while preserving the visual appearance of the cover image.
Among the numerous image steganographic techniques, the Least Significant Bit (LSB) method is widely adopted because
of its simplicity, high embedding capacity, and low computational complexity. However, its sequential embedding
mechanism makes it susceptible to steganalysis and statistical attacks. To address this limitation, pseudorandom embedding
techniques distribute secret information randomly throughout the cover image using a shared secret key, thereby improving
security and imperceptibility. This paper presents a comparative performance analysis of the LSB and Pseudorandom
Encoding techniques for secure image steganography. Both techniques were implemented in MATLAB using identical cover
images and secret messages to ensure an unbiased evaluation. Performance was assessed using Peak Signal-to-Noise Ratio
(PSNR), Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), embedding capacity, and visual image quality.
Experimental results demonstrate that although both techniques effectively conceal secret information with negligible
perceptual degradation, the Pseudorandom Encoding technique consistently produces higher PSNR values and lower
distortion than the conventional LSB method. Furthermore, the random distribution of embedded data significantly
improves resistance to unauthorized detection while maintaining comparable embedding capacity. The findings confirm
that pseudorandom embedding provides a more secure and robust alternative to sequential LSB steganography for digital
image information hiding.
Mujittaba Bature, A. Lawal, T. Lawal· International Journal of Inn...· 0 citations
A new framework that combines hybrid encryption with saliency-based adaptive embedding to select the most effective regions for data concealment in cover images, based on the BossBase dataset is proposed.
Abdullah S. al-Malaise Alghamdi, Rana Alrawashdeh· Journal of Cyber Security an...· 0 citations
Steganography is the practice of hiding secret information within digital media such as images, audio, or video. It
ensures confidential communication by concealing the existence of data itself, unlike encryption. Modern research focuses on
improving invisibility, security, and resistance to detection using AI and deep learning. This project aims to design a secure and
intelligent image steganography system using a hybrid Transformer model. It focuses on increasing data hiding capacity while
maintaining image quality and reducing detectability. The system will be capable of resisting steganalysis attacks, compression,
and noise distortions in real-world usage. Recent studies show that deep-learning steganography models still suffer from low
robustness and poor scalability. Most approaches fail under compression or noise, and their hidden data can be detected by
advanced AI models. High computational cost, limited payload capacity, and dataset dependency further affect their reliability.
This project introduces a hybrid Transformer integrated with Discrete Cosine Transform (DCT) for frequency embedding. By
combining spatial and frequency domains, it ensures better concealment and robustness. Adversarial training with a steganalysis
discriminator will enhance security against modern detection models. The system will achieve higher PSNR and SSIM scores,
proving superior imperceptibility and accuracy.
Implementation will use Python, PyTorch, and OpenCV for model training and image processing. Datasets like COCO,
BOSSBase, and ImageNet will be used for evaluation. Performance metrics such as PSNR, SSIM, MSE, and BER will measure
quality and accuracy. Development and testing will be carried out in Jupyter Notebook or Google Colab environments.
Aakash Bonagiri, N. N. Kumar· International Journal for Re...· 0 citations
Image steganography is an information-hiding technique, aiming to achieve confidentiality and data privacy while transmitting the data in a digital environment. Over the last two decades, steganography has evolved from classical spatial domain embedding to intelligent and adaptive steganographic systems capable of balancing imperceptibility, embedding capacity, robustness and security. This paper presents a review by categorizing the existing techniques into spatial domain-based, transform domain-based, hybrid and adaptive intelligent techniques. The review follows the PRISMA approach to make the selection process transparent for the inclusion and exclusion of papers in the study. Initially, the reviews include the spatial domain-based methods focusing on higher embedding capacity and simple embedding strategy, followed by transform-domain based techniques, including discrete cosine transform, discrete wavelet transform, and other multi-resolution wavelet transforms aiming to enhance robustness and imperceptibility by embedding the data into frequency coefficients. This paper further explores the methods that combine these techniques with other recent trends to develop adaptive and hybrid techniques. These techniques mainly integrate chaotic theory to enhance the security of secret data before embedding and optimization algorithms such as genetic algorithm, particle swarm optimization, Firefly, etc., for adaptive embedding to achieve an improved tradeoff. Finally, intelligent and adaptive techniques based on deep learning models such as convolutional neural networks, autoencoders, and generative adversarial networks are examined, highlighting their ability to learn intelligent embedding strategies and resist modern steganalysis. A comparative analysis is presented, including the technique, strengths, and limitations, together with the discussion of performance evaluation metrics and vulnerability analysis under image processing attacks. The review highlights the current trends and outlines the future direction to develop next-generation secure image steganographic systems.
Shikha Chaudhary, Gunjan Gupta, V. K. Mishra et al.· Signals· 0 citations
A comprehensive review of spatial domain image steganography by examining its historical development, fundamental concepts, classification, and major techniques, including Least Significant Bit (LSB), Adaptive LSB, Pixel Value Differencing (PVD), Pixel Indicator Technique (PIT), Optimal Pixel Adjustment Process (OPAP), edge-based methods, and other adaptive spatial approaches.
N. S., Sidha P. P., A. G et al.· International Journal of Tec...· 0 citations
VARStego is introduced, a reversible dual-layered steganographic framework designed to conceal confidential patient information within medical images, which uses the Huffman algorithm to compress all patient information in a separate layer of the framework, while a localized variance-based algorithm is employed to analyze medical images to find rough and complex regions.
Basten Andika Salim, Adifa Widyadhani Chanda D'Layla, Ntivuguruzwa Jean de La Croix et al.· Computers, Materials & C...· 0 citations
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