Enhancing Image Steganography through Generative Adversarial Networks: Applications and Innovations

Image steganography, a technique for embedding secret information within images, has evolved significantly with the introduction of Generative Adversarial Networks (GANs). Traditional methods often struggled with limitations such as low steganographic capacity and poor image quality. This article explores the integration of GANs into image steganography, focusing on three main applications: carrier modification, carrier selection, and carrier synthesis. GANs enhance the embedding capacity, imperceptibility, and security of steganographic systems by generating encrypted images that are robust against advanced steganalysis techniques. The study examines the advancements and challenges in applying GANs, highlighting the potential for further research and application. It is noted that while GANs offer substantial improvements, the diversity of methods and practical applications remains limited. Future research directions include exploring diverse techniques and enhancing generative models to produce more sophisticated steganographic content that could be integrated into daily use, aiming for broader and more secure applications in secure communications.