An effective GPU-based random grid secret sharing using an autoencoder image super-resolution

Visual crypto-system is a class of cryptography intended to secure images. Random-grid crypto-system is a type of visual cryptosystem that generates an encrypted grid of the secret image utilizing a pre-encoded grid and the secret image. The random grid research is still engaging in three dimensions: security, quality, and efficiency. Though there are many works in improving security, there is scope for investigation in the other two directions from the perspective of technological advancements. There has been a significant increase in the number of Graphical Processing Unit (GPU) cores for which the random grid models are intuitively amenable. The random grid secret sharing models demand more improvement in the quality of the reconstructed image as they achieved only 50% contrast. In this paper, we proposed a GPU based random-grid model to improve its efficiency by exploiting the data-parallelism inherent in the model. In addition to this speedup of 3151x, we restored the secret image with a quality almost equal to the original secret image using autoencoder super-resolution. Objective quality measures such as MSE, NCC, NAE and SSIM for the proposed model empirically confirm the improvement in image quality compared to other state-of-the-art models.