MedicalFuzzySec: A novel steganography technique using fuzzy logic to secure electronic patient data (EPD) concealment in medical images

Abstract

Medical diagnostic systems generate sensitive patient information that requires optimal protection during transmission and storage. Image steganography provides a secure method for embedding secret data, making it imperceptible to the naked eye as part of the original image. However, applying general image steganography directly to medical images can compromise the quality of the transmitted data, and the distortions make the image hosting the secret information appear suspicious and inaccurate for medical interpretation. Steganography in medical images is in its early stages, focusing primarily on basic data-hiding techniques with limited security enhancements. This study introduces MedicalFuzzySec, a dedicated steganographic framework for concealing Electronic Patient Data (EPD) in medical images through fuzzy logic-guided difference expansion. The originality of MedicalFuzzySec lies in its adaptive embedding mechanism, which selectively identifies optimal pixel regions using fuzzy inference rules to ensure high data security with minimal impact on diagnostic image quality. MedicalFuzzySec addresses the limitations of existing approaches, including image degradation and insufficient payload handling, by offering a secure, high-fidelity solution tailored to clinical image standards. Experimental results confirm that MedicalFuzzySec consistently achieves high imperceptibility and robustness, with PSNR values ranging from 56.06 dB to 76.29 dB and SSIM values from 0.989 to 0.999, positioning it as a state-of-the-art solution for secure EPD transmission in medical systems.