2026· Journal of Cyber Security and Risk Auditing· Vol 2026, pp. 196-214· 0 citations
TL;DR
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.
Abstract
Image steganography hides secret data within digital media, such as images, without detection. Traditional stenographic methods struggle with three major problems including limited data capacity, susceptibility to attacks, and compromised visual quality. In this work, we propose a new framework, based on the BossBase dataset, that combines hybrid encryption with saliency-based adaptive embedding to select the most effective regions for data concealment in cover images. We encrypt the secret image in the first step using a hybrid encryption approach, Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithm, where AES is first used to encrypt the secret image and the AES key is then RSA-encrypted for dual layer encryption. Bitwise triplication technique and majority voting are incorporated to protect the encrypted key from bit errors. Then, we generate a hybrid histogram equalization (HE) map from the cover image's saliency map to embed the secret image. During the embedding process, we use the Least Significant Bit (LSB) technique, which selects areas in the hybrid map derived from the cover image with low visual sensitivity for embedding data. The system evaluation includes multiple performance metrics, including Peak-to-Signal Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), Bit Error Rate (BER), payload capacity, and execution time. The experimental results show excellent imperceptibility for all secret image sizes (64×64, 128×128, 192×192) with PSNR values above 58 dB and SSIM values above 0. 9995.The system succeeded in reconstructing completely imperceptible content with BER = 0. The reconstructed contents had infinite PSNR and SSIM = 1.0000 against any image Perturbation including noise, blurring, compression, and cropping. All the conventional steganalysis and the deep-learning-based steganalysis (RS, CRM, Xu-Net, Ye-Net) failed to detect the embedded signals.
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
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
Reversible data hiding in encrypted images (RDHEI) often creates embedding room by preserving or preprocessing image redundancy, thereby coupling the hiding layer to a specialized encryption model. This paper presents a dual-image method that works directly on AES-CTR ciphertext. Its design rests on four elements: a keyed Sudoku coordinate code for carrying two base-8 digits per accepted pair; exact reconstruction of each ciphertext pair from inter-block displacement; AES-GCM framing for payload confidentiality and integrity; and an adaptive movement threshold that selects the lowest-distortion mappings able to accommodate the complete frame. The data hider requires neither plaintext nor the image-encryption key, and no location map is transmitted. On sixteen 512×512 grayscale images, the method achieves a mean maximum net rate of 1.4187 bits per transmitted pixel. At ERt=1.0, the two marked outputs reach mean PSNR values of 47.65 dB, and all 160 image-rate trials yield zero payload error and pixel-exact recovery. An independent implementation of the embedding and recovery layer of Venkatesh et al. is also examined; published and reproduced results are reported separately where the original specification leaves ambiguity. The experiments show that the proposed architecture provides predictable payload, low carrier-domain distortion, authenticated framing, and exact recovery while retaining standard image encryption.
Cao Thi Luyen· Journal of Science and Techn...· 0 citations
VisionStego is proposed, a duallayer security architecture that pairs symmetric-key encryption with an artificial-intelligence-guided steganographic embedding stage, so that cloud-hosted data is protected in both substance and appearance.
R. Saxena, Priti Maheshwary· International Journal for Re...· 0 citations
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.
S. S., Meruvu Sai Kumar· International Journal of Sci...· 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
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