Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography

Abstract

Based on Hilbert Random Secure Distribution, a novel data-hiding method for embedding secret information about the patient in a cover image MRI sample has been proposed. Least significant bit (LSB) and most significant bit (MSB) techniques are applied for the physical hiding. Medical images confidentiality suffers from potential attacks and tracing by an unauthorized access. Technically, distributing the secret text in a random way on the cover image is the core security function of the proposed model. In order to evaluate the performance of the proposed solution, three quality metrics: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), percentage residual difference (PRD) and Structural Similarity Index measure (SSIM) were computed and compared on ten MRI images. Experimental results showed significant results in comparison with other models and reached average PSNR up to 61 db. Furthermore, the security analysis in case of $512\times 512$ image samples show complex probability of distribution based on the Hilbert space model.

Keywords

• Patient privacy
• steganography
• LSB
• MSB
• MRI samples
• Hilbert similarity