Reversible data hiding exploiting Huffman encoding with dual images for IoMT based healthcare

Abstract

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.