Continuous technological progressions and huge investments are made for the realization of the various goals in the Internet of Things (IoT) driven networks. Internet of Medical Things (IoMT) being a part of IoT has made human living smarter. It is revolutionizing the healthcare industry and is providing a smarter healthcare framework to the people. The generic IoMT framework consists of the major components i.e., data acquisition, communication gateways, and servers. Once the data is acquired, it is sent over the insecure channel where its authentication is essential before diagnosis. In this work, a dual image reversible data hiding technique with high capacity is proposed for IoMT based networks. First of all, the acquired secret data is preprocessed using the Huffman encoding strategy. Once Huffman coding is applied, a codebook of ‘d’ bits is generated for encoding the converted decimal values using indices. The value of these indices is divided into 2 parts and embedded into two visually similar images to obtain dual stego images. The scheme has provided a very high payload while maintaining good perceptual quality. The results obtained depict significant improvement compared to the state-of-the-art. The scheme provides an average (percentage) improvement in embedding capacity by 33.2%, with the improvisation of Peak Signal to Noise (PSNR) Ratio by 1.32%. The average value of the Structural Similarity Index (SSIM) is found to be 0.8873. The scheme is computationally efficient which makes it a better candidate to be used in IoMT driven networks.

为了实现物联网（IoT）驱动的网络中的各种目标，需要进行持续的技术进步和巨额投资。医疗物联网（IoMT）作为物联网的一部分，使人类生活变得更加智能。它正在彻底改变医疗保健行业，并为人们提供更智能的医疗保健框架。通用IoMT框架由主要组件组成，即数据采集，通信网关和服务器。数据一旦获取，便会通过不安全通道发送，在此之前，必须先进行身份验证，然后才能进行诊断。在这项工作中，针对基于IoMT的网络，提出了一种具有高容量的双图像可逆数据隐藏技术。首先，使用霍夫曼编码策略对获取的机密数据进行预处理。应用霍夫曼编码后，生成“ d”位的代码簿，以使用索引对转换后的十进制值进行编码。这些指标的值分为两部分，并嵌入到两个视觉相似的图像中，以获得双重隐身图像。该方案提供了很高的有效负载，同时保持了良好的感知质量。与最新技术相比，获得的结果显示出显着的改进。该方案将嵌入容量平均提高了33.2％，而峰值信噪比（PSNR）也提高了1.32％。发现结构相似指数（SSIM）的平均值为0.8873。该方案计算效率高，这使其更适合用于IoMT驱动的网络。这些指标的值分为2部分，并嵌入到两个视觉相似的图像中以获得双重隐身图像。该方案提供了很高的有效负载，同时保持了良好的感知质量。与最新技术相比，获得的结果显示出显着的改进。该方案将嵌入容量平均提高了33.2％，而峰值信噪比（PSNR）也提高了1.32％。发现结构相似指数（SSIM）的平均值为0.8873。该方案计算效率高，这使其更适合用于IoMT驱动的网络。这些指标的值分为2部分，并嵌入到两个视觉相似的图像中以获得双重隐身图像。该方案提供了很高的有效负载，同时保持了良好的感知质量。与最新技术相比，获得的结果显示出显着的改进。该方案将嵌入容量平均提高了33.2％，而峰值信噪比（PSNR）也提高了1.32％。发现结构相似指数（SSIM）的平均值为0.8873。该方案计算效率高，这使其更适合用于IoMT驱动的网络。与最新技术相比，获得的结果显示出显着的改进。该方案将嵌入容量平均提高了33.2％，而峰值信噪比（PSNR）也提高了1.32％。发现结构相似指数（SSIM）的平均值为0.8873。该方案计算效率高，这使其更适合用于IoMT驱动的网络。与最新技术相比，获得的结果显示出显着的改进。该方案将嵌入容量平均提高了33.2％，而峰值信噪比（PSNR）也提高了1.32％。发现结构相似指数（SSIM）的平均值为0.8873。该方案计算效率高，这使其更适合用于IoMT驱动的网络。