Dr. Balaji K, Miss Dhanushree H N, Miss Rachana BM
Image steganography, which involves the embedding of data into digital images in such a way that the embedding remains undetectable, has seen dramatic changes over the past several years due to advances in deep learning, generative adversarial networks (GANs), diffusion-based generative modelling, and transformer architectures.This paper aims to synthesize recent progress within four main categories of image steganography spatial domain, transform domain, deep-learning-based, and coverless steganography via a review of 40 recent papers published from 2021 to 2025.The methods are reviewed according to a common set of metrics Peak Signal-To-Noise Ratio (PSNR), structural similarity index (SSIM), embedding capacity, invisibility, robustness against steganalysis, and computation cost.Special attention is devoted to four rapidly developing subdomains: diffusion-based generative steganography, invertible neural networks (INNs), adversarially trained hidden mechanisms, and secure methods with provable guarantees.The review additionally includes a structured taxonomy of the field, a comparative analysis based on visual data, a list of popular datasets, an overview of open challenges, and directions for future work.