A Novel Steganography Method Without Embedding Using Generative Adversarial Networks - Sreedhar, D; Padmanabham, P; Murthy, J.V.R (Journal of Neonatal Surgery, 2025)
Abstract: Steganography traditionally relies on embedding secret information into a cover medium, introducing potential distortion and susceptibility to detection. This paper proposes a novel steganographic method that avoids explicit embedding, leveraging the capabilities of Generative Adversarial Networks (GANs) to generate images that inherently represent the hidden message. The proposed method utilizes a conditional GAN framework where the secret message serves as a condition to generate a visually plausible image. We present a comprehensive literature review, detail the architecture of the proposed system, and validate its effectiveness through rigorous experiments. Comparative analysis with traditional and recent deep learning-based methods highlights the superiority of the proposed approach in terms of security, imperceptibility, and payload capacity.