Article Dans Une Revue IEEE Transactions on Instrumentation and Measurement Année : 2025

Restoration-Enhanced Reversible Information Steganography Network for CT Images in the Internet of Medical Things

Résumé

Smart health and emotional care powered by the Internet of Medical Things (IoMT) are revolutionizing the healthcare industry by adopting several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The security of medical images containing private patient information distributed in IoMT is of paramount importance in social communication, and guaranteeing their reliability has attracted widespread attention. Some existing medical image steganography methods fail to guarantee the original image’s integrity, authenticity, and lossless restoration. To solve this problem, a restoration-enhanced reversible adaptive information steganography network, MediSR-Net, is proposed. Specifically, MediSR-Net consists of an analysis module and an encoding module. The image degradation-restoration strategy is exploited by the analysis module to improve the effectiveness of information embedding and perceptual field to reduce the error of pixel prediction. An information distillation and attention machine-based approach are adopted to achieve degradation-restoration of computed tomography (CT) medical images at any scale and focus more on the degradation-restoration effect with small magnification factors. The encoding module is responsible for the embedding and extraction of privacy information. Ten state-of-the-art are compared with the MediSR-Net method concerning steganographic effect, embedding rate, distortion analysis, and prediction error. The qualitative and quantitative results demonstrate the effectiveness and superiority of MediSR-Net.

Fichier principal
Vignette du fichier
Chen et al. - 2025 - Restoration-enhanced reversible information steganography network for CT images in the Internet of M.pdf (2.53 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-05113000 , version 1 (17-06-2025)

Licence

Identifiants

Citer

Kai Chen, Yang Chen, Gouenou Coatrieux, Jiasong Wu, Jean-Louis Coatrieux. Restoration-Enhanced Reversible Information Steganography Network for CT Images in the Internet of Medical Things. IEEE Transactions on Instrumentation and Measurement, 2025, 74, pp.4011615. ⟨10.1109/TIM.2025.3542878⟩. ⟨hal-05113000⟩
157 Consultations
183 Téléchargements

Altmetric

Partager

  • More