Jul 2026· International Journal of Science and Research Archive· 0 citations
TL;DR
A secure image steganography framework that integrates Hybrid Edge-Guided Fourier-Domain Steganography Framework with the Convolutional Neural Network (CNN) based learned detector for securing the process of image steganography is proposed.
Abstract
Image steganography focuses on hiding hidden information in digital images without degrading the quality of images and avoiding any possibility of detecting the hidden message. Traditional edge-based methods adopt predefined algorithms like Sobel and Canny for detecting the regions suitable for embedding, which may not work efficiently if the images have weak and non-clear edges. Besides, traditional spread spectrum techniques require predefined pseudo-noise (PN) sequences as secret keys, which may pose a threat to security and robustness against any geometric transformations. To address these limitations, This paper proposes a secure image steganography framework that integrates Hybrid Edge-Guided Fourier-Domain Steganography Framework with the Convolutional Neural Network (CNN) based learned detector for securing the process of image steganography. A content adaptive attention mechanism will be adopted to detect the best regions of embedding in the spatial domain, whereas PN classes embedding will be done in the Fourier domain. The CNN based encoder-decoder network will be used to hide and recover the data, and a trained CNN classifier will allow a blind detection. Efficiency and security of this approach will be measured by calculating PSNR, SSIM, MSE, BPP and Re using Xu-Net and Ye-Net steganalysis models.
Comparative results with existing state-of-the-art methods show that this end-to-end embedding approach delivers promising performance in terms of human visual accuracy, with a PSNR score of 37.054 and an SSIM score of 0.9754.
Noor Fahem Sahib, S. Hashem, E. F. Naser· Baghdad Science Journal· 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
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.
Dena Abu Laila, Ziad Dawahdeh, Amer Alqutaesh et al.· Journal of Cyber Security an...· 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
Coverless image steganography hides secret information without directly changing the cover image. Because the original image is not modified, it is more difficult for image steganalysis tools to detect the hidden information. In this research, Wasserstein Generative Adversarial Networks (WGAN) and Local Binary Pattern (LBP) features are used to securely hide and transmit sensitive information using a single cover image. The proposed method provides higher hiding capacity and better security than traditional methods that require multiple cover images.First, the cover image is divided into overlapping blocks. The LBP feature is calculated for each block and converted into a hash code. The secret message is then matched with image blocks having the same hash codes. A lookup table is used to make the embedding process faster.The generated stego information is given to a GAN model to create a meaningful but unrelated image. This image can be sent to the receiver instead of directly sending the stego image. At the receiver side, the WGAN model is used to reconstruct the required stego information. The use of overlapping blocks helps generate many unique hash codes. Without overlapping blocks, a single image may not provide enough hash codes to hide the complete secret message.
Vijaysinh Jadeja, Khyati Rami, Swati Patel et al.· International journal of com...· 0 citations
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