Deep reinforcement learning–based reversible medical image encryption framework for secure IoMT environments

The Internet of Medical Things (IoMT) environments face significant challenges in securely transmitting and storing medical images due to limited computational resources, multiple device types, and increasing cybersecurity threats. This paper describes a reversible RGB medical image encryption framework that employs deep reinforcement learning by combining adaptive policy learning with deterministic cryptographic algorithms. A Deep Q-Network (DQN) is used to dynamically select encryption actions