Secure image transmission based on visual cryptography scheme and artificial neural network-particle swarm optimization-guided adaptive vector quantization

Abstract

Image transmission holds a major share in data communication, and thus secure image transmission is currently a challenging domain of research. A secure image transmission scheme is proposed that physically transmits the encrypted image employing visual cryptography scheme (VCS). During physical transmission, the meaningless shares may attract curious hackers and if captured and stacked, the secret may be revealed. Moreover, the increase in transmission overhead due to multiple share images resulted from a single secret image after encryption is another concern regarding the physical implementation of VCS. Focusing on both observations, vector quantization (VQ) is used to encode as well as to compress each of the shares before transmission. To utilize VQ, its two parameters, cell width and dimension of grid, are needed to be optimized for various kind of images without compromising the randomness property of the shares. Hence, a particle swarm optimization-guided VQ is proposed, and furthermore, a multilayer perceptron in conjunction with an autoencoder are also trained in synchronism with that to automatically obtain the optimal VQ for each image type during the transmission. The proposed scheme is successfully implemented with different types of images for secure physical transmission with a 62.8% data volume reduction and 98.07% image quality retrieval.