Abstract

The process of identifying concealed data in digital media, or steganography, has grown in importance in the world of cybersecurity as a result of advanced steganography algorithms. Current, sophisticated picture editing techniques are beyond the capabilities of traditional steganalysis techniques, which frequently rely on manual feature extraction and statistical analysis. In particular, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs) are thoroughly analyzed in this review paper as useful methods for steganalysis in color images. In comparison to conventional methods, hidden data may now be detected with greater accuracy and automation thanks to recent developments in deep learning architectures like ResNet, LSTM, and CycleGAN. These models' advantages, disadvantages, and shortcomings are shown by a comparison study. Key problems such high computing costs, interpretability of models, and the requirement for different datasets are also covered in the paper. More inventive training methods, more dataset diversity, and the creation of hybrid models are some of the future directions that are mentioned. The research highlights the critical role that deep learning plays in steganalysis and opens thedoor to more resilient and flexible cybersecurity detection systems.