Reversible Watermarking Framework for Secure Smart Healthcare

The rapid integration of the Internet of Things (IoT) and Artificial Intelligence (AI) into healthcare, protecting medical data integrity, authenticity, and confidentiality has become a critical challenge. Digital watermarking offers a promising solution by embedding authentication data directly into medical images and health records. However, irreversible watermarking may distort diagnostic information, making it unsuitable for clinical use. This paper proposes a Reversible Watermarking Framework (RWF) tailored for secure smart healthcare applications, ensuring both data authenticity and lossless recovery of medical images. The proposed framework integrates Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) with a hash-based authentication module to enhance robustness and reversibility. Experimental evaluations on benchmark medical datasets demonstrate improved performance in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and watermark extraction accuracy, validating its suitability for smart healthcare environments.