Pixel shift-based reversible steganography for privacy-preserving medical images

Abstract

Steganography plays a critical role in preserving the privacy of medical images by protecting sensitive patient information without compromising image quality or diagnostic utility. This paper proposes a novel lossless privacy protection algorithm for medical images based on pixel displacement. The core innovation lies in the synergistic combination of physical transformation and deep learning—specifically, the integration of shift-rectification with a dual-carrier embedding mechanism. The proposed method begins by applying byte-level pixel shifting to the confidential medical image to generate a shift-rectified version. A deep convolutional neural network is then employed to embed both the original and the rectified medical images into two separate carrier images, ensuring that the resulting container images are visually indistinguishable from the carriers. Subsequently, a dedicated recovery network extracts and reconstructs the lossy shift-rectified image and a degraded version of the original from the two carriers, using the former to calibrate and fully restore the original medical image in a lossless manner. Experimental results demonstrate the algorithm’s effectiveness in preserving privacy while enabling perfect image recovery: the container images achieve an average PSNR of 40.08 dB and MSSIM of 0.988 relative to the carriers, confirming near-perceptual-indistinguishability; the reconstructed secret images are recovered with a bit-level accuracy of 100%; and the algorithm maintains robustness under Gaussian noise, salt-and-pepper noise, and PCA compression with lossless recovery guaranteed. Moreover, the method shows excellent generalization across multiple medical imaging datasets (CT, MRI, and X-ray), highlighting its clinical applicability and innovation in secure medical image transmission systems.