Deep Learning-Based Segmentation for Medical Data Hiding With Galois Field

Data hiding is of the utmost importance for protecting the copyright of image content, given the widespread use of images in the healthcare domain. Presently, medical image security is important not only for protecting individual privacy but also for accurate diagnosis and treatment. In this paper, a deep learning-based segmentation for a medical data hiding technique with the Galois field is proposed. This technique uses a customised UNet3+ deep learning network to segment a medical image into a Region of Interest and a Non-Region of Interest. Through the proper spatial and transform-based embedding method, multiple marks are embedded into both parts of the medical image. In addition, encryption is utilised to provide additional security for protecting sensitive information when transmitted over an open channel so that the information cannot be retrieved. The extensive experimental results show that the proposed technique for medical images achieves a good balance between imperceptibility and robustness with high security. Further, the obtained results showed the superiority of our technique over state-of-the-art techniques, demonstrating that it can provide a reliable security solution for healthcare data.