An Explainable Deep Learning Framework for Usable and Secure Image Steganography

In the evolving landscape of digital security, image steganography has gained prominence as a technique to conceal information within media content. However, existing deep learning-based approaches often prioritize imperceptibility and robustness while overlooking critical aspects like usability and interpretability. This paper presents a novel explainable deep learning framework for image steganography that integrates human-centered design principles and usability-aware embedding constraints. Our architecture combines a modified CycleGAN-based embedding mechanism with interpretable attention modules to provide visual explanations of hidden data regions. Additionally, the system is designed with usability metrics in mind minimizing embedding time, simplifying user interaction, and ensuring reversibility and transparency. To evaluate the proposed framework, we conduct comprehensive experiments using the BOSSbase dataset and benchmark it against state-of-the-art methods on PSNR, SSIM, capacity, and steganalysis resistance. Furthermore, a usability evaluation involving real users captures perceived ease-of-use and interaction clarity. Results demonstrate that our method not only maintains high imperceptibility and security but also offers better explainability and operational simplicity than conventional deep learning models. This work bridges the gap between technical performance and real-world applicability, contributing toward the development of secure, user-friendly, and transparent AI-driven steganographic systems.