A Contribution to Coverless Image Steganography based on Generative AI

This paper presents an approach to Coverless Image Steganography (CIS) leveraging Generative Adversarial Networks (GANs) for secure and imperceptible information hiding. Unlike traditional steganography methods that embed data into existing images, CIS methods generate new images directly conditioned on the hidden content, thereby avoiding detectable modifications. Using satellite imagery, we evaluate three encoder architectures, namely basic, residual, and dense, within the SteganoGAN framework to assess their effectiveness in preserving image quality and extraction fidelity. The results show the potential of GAN-based CIS and providing insights into the trade-offs between visual quality and hidden message capacity.