Abstract With the rapid adoption of smart healthcare systems, medical image distribution is expected to become increasingly prevalent. However, such distribution faces significant threats of privacy breaches. Although conventional encryption schemes can prevent unauthorized access during transmission, they cannot control the illegal redistribution of decrypted content. To prevent medical images from being accessed by unauthorized personnel, we propose a dual-level security scheme that integrates robust watermarking with selective encryption for smart healthcare environments. The primary objective is to protect medical images throughout their entire lifecycle. The key innovation of our approach lies in its ability to perform watermarking operations directly on encrypted content. Additionally, the scheme is designed to satisfy diverse security requirements across various medical scenarios. This dual-level security mechanism enables tracing of medical content usage and controlling unauthorized redistribution. Comprehensive experimental evaluations and security analyses demonstrate that our method outperforms existing approaches in computational efficiency while maintaining comparable security levels. This research contributes a novel methodology for end-to-end protection of medical images and holds significant implications for multimedia security in healthcare contexts.

Abstract Driven by the requirements for entirely low communication latencies, high bandwidths, reliability and capacities, the Fifth Generation (5G) networks has been deployed in a number of countries. One of the most prevalent application scenarios of 5G networks is the Internet of Things (IoT) that can potentially boost convenience and energy savings. However, the information exchanged over the open wireless 5G networks is susceptible to numerous attacks such as malicious modifications. Although many protocols have been developed to protect against these attacks, the provision of optimum security and privacy issues in 5G networks is still an open challenge. This is attributed to the high device density, frequent handovers and resource constrained nature the 5G IoT nodes. In this article, a network selection and authentication protocol that securely verifies the authenticity of all the communicating entities is presented. The network selection is accomplished using Artificial Neural Network (ANN) for increased efficiency. In addition, all the security tokens are independently derived at the end devices without the involvement of any central authority. Formal security analysis based on the Burrows–Abadi–Needham (BAN) logic shows that all the terminals securely authenticate each other before the onset of packet exchanges. In addition, it is shown that this protocol thwarts majority of the conventional 5G attack vectors and is robust under the Dolev–Yao (DY) threat model. Moreover, a comparison with other related schemes shows that the proposed protocol offers many adorable security features at relatively low communication and computation costs. The simulation results show that the deployed ANN yields low packet loss ratio and latency variations.