Protecting Consumer Healthcare Imaging Data From AI-Tampering via Blind Reversible Watermarking

Medical images are increasingly processed across consumer-facing healthcare pathways—teleradiology portals, mobile viewers, MIoT and edge gateways. These images are vulnerable to tampering, privacy abuse, and AI-driven manipulation, such as GAN forgeries and adversarial perturbations, that can mislead diagnosis. We propose a blind, reversible watermarking framework that employs a rotation-invarian...