Steganography-Based Transmission of Medical Images Over Unsecure Network for Telemedicine Applications

Steganography is one of the best techniques to hide secret data. Several steganography methods are available that use an image as a cover object, which is called image steganography. In image steganography, the major features are the cover object quality and hiding data capacity. Due to poor image quality, attackers could easily hack the secret data. Therefore, the hidden data quantity should be improved, while keeping stego-image quality high. The main aim of this study is combining several steganography techniques, for secure transmission of data without leakage and unauthorized access. In this paper, a technique, which combines various steganography-based techniques, is proposed for secure transmission of secret data. In the pre-processing step, resizing of cover image is performed with Pixel Repetition Method (PRM). Then DES (Data Encryption Standard) algorithm is used to encrypt secret data before embedding it into cover image. The encrypted data is then converted to hexadecimal representation. This is followed by embedding using Least Signification Bit (LSB) in order to hide secret data inside the cover image. Further, image de-noising using Convolutional Neural Network (CNN) is used to enhance the cover image with hidden encrypted data. Embedded Zerotrees of Wavelet Transform is used to compress the image in order to reduce its size. Experiments are conducted to evaluate the performance of proposed combined steganography technique and results indicate that the proposed technique outperforms all existing techniques. It achieves better PSNR, and encryption/decryption times, than existing methods for medical and other types of images.