Secure and imperceptible medical image watermarking via multiscale QR embedding and attention-based optimization

The exponential growth of telemedicine and digital health platforms has introduced serious challenges in maintaining the confidentiality, authenticity, and diagnostic integrity of medical images transmitted over insecure networks. This study specifically addresses these challenges by developing a blind and imperceptible watermarking architecture that ensures both data privacy and image reliability. The proposed method integrates four complementary techniques—Non-Subsampled Shearlet Transform (NSST) for multiscale feature extraction, QR decomposition for numerically stable embedding, Particle Swarm Optimization (PSO) for adaptive block selection, and Grad-CAM attention maps for perceptual guidance. Together, these components solve three long-standing issues in medical image protection: (1) preserving diagnostic quality while embedding sensitive data, (2) achieving robustness against signal and geometric distortions without reference to the original image, and (3) reducing computational complexity for real-time telemedicine integration. The watermark encodes both compressed patient metadata and biometric images using BCH error correction and XOR encryption. Experiments on colorized CT and X-ray datasets show high imperceptibility (PSNR = 45.21 dB, SSIM = 0.9864), strong robustness (NCC ≥ 0.897), and fast runtime (≈ 2 s per image), confirming the method’s suitability for secure and practical clinical deployment.