Advancing Imperceptible Data Hiding: A Novel GAN-Based Framework for Robust and High-Capacity Steganography in Digital Media

In this work, we propose a novel GAN-based framework for imperceptible data hiding, designed to enhance the robustness, capacity, and security of steganography in digital media. Current methods often suffer from trade-offs between the imperceptibility of hidden data and its resilience to common image transformations. Our approach leverages advanced Generative Adversarial Networks to embed high-capacity data in a manner that remains undetectable under rigorous scrutiny and resilient to typical distortions such as compression, resizing, and filtering. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques, achieving superior results in terms of both visual quality and data retrieval accuracy. This work not only advances the field of steganography but also sets the stage for future developments in secure data communication and digital watermarking.