2026· Computers, Materials & Continua· pp. 1-10· 0 citations· 47 references
TL;DR
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.
Abstract
: The modernization of communication and healthcare environments has introduced critical security challenges, as all transmission is almost certainly done through digital networks prone to attacks. To counteract this, researchers have worked to create methods of data concealment, hiding the existence of sensitive data itself from prying eyes. However, many of these methods lack the necessary ability to balance imperceptibility and payload capacity. In addition, different from generic images, medical images must preserve their structural and visual integrity, requiring frameworks that prioritize maintaining high similarity between images or, at times, complete recovery of the original image. This paper introduces VARStego, a reversible dual-layered steganographic framework designed to conceal confidential patient information within medical images. VARStego 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. Utilizing these regions, the VARStego embeds data within selected pixels using a redundancy-based algorithm, further improving payload capacity. The proposed VARStego method is tested on the DICOM Library dataset, containing Digital Imaging and Communications in Medicine (DICOM) formatted files and the Kaggle dataset, providing computed tomography (CT) medical images. VARStego achieved a Peak Signal-to-Noise Ratio (PSNR) of 72.192 dB and a perfect Structural Similarity Index Measure of up to 1, ensuring that image quality is maintained. These scores show minimal degradation as the payload size increases from 1 to 50 kilobyte for American Standard Code for Information Interchange (ASCII) payload and 1 to 100 kilobit for bitstring payload, demonstrating the method’s consistent performance.
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
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
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
The growth of development the digital communication has greatly raised the chances of information leakage, interception, and unauthenticated access, particularly in heterogeneous multimedia setting. Conventional single-modal steganographic methods have been known to be limited in their payload storage capacity, weak, and susceptible to steganalysis attacks. In order to overcome these issues, the proposed work develops a Robust Multimodal Steganography Framework to ensure the secure transmission of the text, image, and audio information over the untrusted communication channels. The suggested framework combines the utilization of adaptive embedding plans in various carrier media with the Discrete Wavelet Transform (DWT) being employed in image stealth, the Discrete Cosine Transform (DCT) being employed in audio embedding and AES-based encryption being employed in protecting the textual payload. An active payload allocation scheme provides maximum imperceptibility and resistance to noise, compression and signal distortions. The experimental analysis of the proposed framework shows that this framework has better security and robustness than traditional unimodal methods. With the system, attains an average PSNR of 52.6 dB with stego-images, SNR of 41.8 dB with stego-audio and a bit error rate of less than 0.7 with common attack conditions like Gaussian noise and compression. It has an enhanced payload capacity of up to 32 and the transparency to perceptions is still high. The framework also has high resistance to statistical steganalysis and the detection accuracy dwindles to less than 18 %.
Prashant Wakhare, G. Sambare, Riyazahemad A. Jamadar et al.· ITEGAM- Journal of Engineeri...· 0 citations
Steganography attempts to conceal messages in plain sight while steganalysis seeks to identify them or, more importantly, to extract the embedded data. Low-payload and spatially localized steganographic embedding is increasingly used to evade detection by classical steganalysis methods. While such strategies preserve global image statistics and remain visually imperceptible, they can disrupt natural pixel-level behavior. This work proposes a behavioral steganalysis framework inspired by process mining that detects image steganography by analyzing localized behavioral deviation using regional behavioral contrast and behavioral amplification. Experiments on lossless grayscale PNG images from the USC SIPI database and 10,000 images from the BOWS2 dataset using 1-bit LSB embedding show that the proposed framework reliably identifies steganographic embedding. On the USC SIPI dataset, conventional statistical detectors, including chi-square analysis and the StegExpose tool, showed limited detection capability under the evaluated localized embedding settings. Despite high perceptual quality of stego images (PSNR > 55 dB), significant behavioral deviation is consistently observed within embedded regions. These results demonstrate that the proposed process mining-inspired framework provides an interpretable and complementary direction for image steganalysis, particularly under low-payload and localized embedding scenarios.
Shikha Badhani, Vinita Verma, Manju Bhardwaj et al.· International Journal of Mat...· 0 citations
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