A Medical Image Steganography Scheme with High Embedding Capacity to Solve Falling-Off Boundary Problem Using Pixel Value Difference Method

Medical images have a vital role in the healthcare industry. The medical sector uses the internet to facilitate the distant sharing of medical information among hospitals and clinics and provide patients with e-health services. We must share a patient’s report secretly so that the intruders can’t steal the patient’s data. The pixel value differencing technique is utilised in this study to store a patient’s medical information report in various medical imaging, such as ultrasound images, computed tomography scans, X-rays, magnetic resonance images, electrocardiographs, and microscopic images. The fundamental objective is to maintain the visual appearance of the medical images so that physicians can analyse and give accurate results and extract information reports precisely. This PVD scheme works on different types of image formats such as Portable Network Graphics (PNG), Joint Photographic Experts Group (JPG or JPEG), BitMaP (BMP), and Tag Image File Format (TIFF). Measurement metrics such as embedding capacity, the difference in histograms between the stego and the cover image, and the peak signal-to-noise ratio (PSNR) are employed to evaluate the effectiveness of the suggested method. On a series of medical images, we have tested this new PVD approach and found that it provides significant payload capacity with the high visual quality of the stego image. The majority of PVD techniques described in the literature only apply to grayscale images, and those that apply to RGB images have falling off boundary problem. RGB images have pixel values that span from 0 to 255, but when the pixels are modified using the PVD technique, sometimes these pixel values fall outside of this range, which causes erroneous results to be obtained during extraction. Additionally, utilising a difference in the histograms of the stego and the cover image, the attacker in a typical PVD technique can disclose the existence and length of the secret message. This novel PVD methodology tackles the classic PVD technique’s falling-off boundary issue and provides some security to the secret message from the histogram quantisation attack.