Spatial Domain Image Steganography: A Comprehensive Review

A Review on Image Steganography Techniques: Evolution from Classical to Adaptive Methods

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.

Image Steganography Under Multiple Performance Metrics: A Comparative Study

Image steganography performance depends on a trade-off among embedding capacity, imperceptibility, structural fidelity, robustness, security, and computational cost. This paper presents a comparative evaluation framework for spatial-domain and transform-domain image steganography using four standard benchmark images: Lena, Baboon, Barbara, and Cameraman. Representative spatial methods, Least Significant Bit (LSB) substitution and Pixel Value Differencing (PVD), are compared with transform-domain methods based on the Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and a hybrid DWT–DCT approach. The evaluation parameters include embedding capacity, bits per pixel, PSNR, SSIM, extraction accuracy, robustness, and execution complexity. Equations, image-wise tables, average-performance tables, and bar charts are provided. The numerical comparison tables are explicitly presented as an illustrative experimental template because measured values require implementation under identical payload, image size, and attack conditions. Keywords: Steganography; LSB; PVD; DCT; DWT; Embedding Capacity; PSNR; SSIM; Performance Evaluation

VARStego: A Reversible Local Variance-Based Steganographic Method

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.

Robust Image Steganography against Differential Attacks Using GA-Optimized LSB Embedding

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.

Cross-domain steganography for hiding images within videos

Performance Analysis of Image Steganography Based on Least Significant Bit and Pseudorandom Encoding Techniques

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.