A Reversible Data Hiding Framework for Secure Brain MRI Protection Utilizing Deep Learning
Received: 18 August 2025 | Revised: 12 October 2025 and 1 December 2025 | Accepted: 3 December 2025 | Online: 9 February 2026
Corresponding author: N. Rama Rao
Abstract
This study presents ConOs-Net, a deep learning–based reversible data hiding framework for secure brain MRI image protection. The model integrates CNN-based residual learning with the Osprey Optimization Algorithm (OOA) to achieve high-fidelity embedding and extraction. Experimental evaluation on the BRATS dataset demonstrates superior performance, achieving a PSNR of 46.38 dB, SSIM of 0.993, low BER, and high embedding capacity. The proposed approach effectively preserves diagnostic quality while ensuring full reversibility and data confidentiality, making it suitable for secure medical image transmission.
Keywords:
medical imaging, reversible, PSNR, SSIM, optimization, deep learning, MRIDownloads
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