Two New Algorithms for Effective Information Hiding in Chest X-Ray Images

Several medical consultations and examinations have been undertaken online due to the Covid outbreak. However, when private data was communicated over the internet or uploaded to the cloud, medical information became more susceptible to security risks. Steganography is a technique for concealing information inside a cover medium such as images. This paper presents two new algorithms (kN-LSB and HDCT) to enhance steganography's performance in medical images. The first algorithm introduces a new approach for systematically dispersing a concealed number of bits across multiple separate locations, while the second algorithm enhances the first by incorporating the capacity to process a full-size hidden image. Each algorithm includes three different settings. The experimental results demonstrated a significant improvement over the standard LSB method, since the error of the first algorithm is nearly zero, resulting in a hidden image reconstruction of extremely high quality. In addition, the suggested algorithm has a payload capacity of 100% and a very low cover changing rate. For applications in which a full-size hidden image must be concealed, we recommend the second algorithm, which has a high payload capacity of 98.96%. This research is beneficial since it contributes to a medical application for enhancing the security of information concealed in chest X-ray images. Medical personnel can generate an image that conceals patient information in a secure manner. Another advantage of our proposed methods is that no training datasets are required.