AGFI-GAN: An Attention-Guided and Feature-Integrated Watermarking Model Based on Generative Adversarial Network Framework for Secure and Auditable Medical Imaging Application

With the rapid digitization of healthcare, the secure transmission of medical images has become a critical concern, especially given the increasing prevalence of cyber threats and data privacy breaches. Medical images are frequently transmitted via the Internet and cloud platforms, making them susceptible to unauthorized access, tampering, and theft. While traditional cryptographic techniques play a vital role, they are often insufficient to fully ensure the integrity and confidentiality of these sensitive images. In this paper, we present AGFI-GAN, a robust and secure framework for medical image watermarking that leverages attention-guided and Feature-Integrated mechanisms within a Generative Adversarial Network (GAN). Specifically, a Feature-Integrated Module (FIM) is proposed to effectively capture and combine both shallow and deep image features to facilitate multilayer fusion with the watermark. The dense connections within the module facilitate feature reuse, boosting the system’s robustness. To mitigate distortion from watermark embedding, an Attention Module (AM) is utilized, generating an attention mask by extracting global image features. This attention mask prioritizes features in less prominent and textured regions, allowing for stronger watermark embedding, while other features are downplayed to enhance the overall effectiveness of the watermarking process. The framework is evaluated based on its versatility, embedding capacity, robustness, and imperceptibility, and the results confirm its effectiveness. The study shows a marked improvement over the baseline, thus highlighting the framework’s superiority.