A Lightweight Secure Image Super Resolution using Network Coding

Abstract

Images play an important part in our daily life. They convey our personal stories and maintain meaningful objects, events, emotions etc. People, therefore, mostly use images as visual information for their communication with each other. Data size and privacy are, however, two of important aspects whilst transmitting data through network like internet, i.e. the time prolongs when the amount of data are increased and the risk of exposing private data when being captured and accessed by irrelevant people. In this paper, we introduce a unified framework, namely Deep-NC, to address these problems seamlessly. Our method contains three important components: the first component, adopted from Random Linear Network Coding (RLNC), to protect the sharing of private image from the eavesdropper; the second component to remove noise causing to image data due to transmission over wireless media; and the third component, utilising Image Super-Resolution (ISR) with Deep Learning (DL), to recover high-resolution images from low-resolution ones due to image sizes reduced. This is a general framework in which each component can be enhanced by sophisticated methods. Simulation results show that an outperformance of up to 32 dB, in terms of Peak Signal-to-Noise Ratio (PSNR), can be obtained when the eavesdropper does not have any knowledge of parameters and the reference image used in the mixing schemes. Various impacts of the method are deeply evaluated to show its effectiveness in securing transmitted images. Furthermore, the original image is shown to be able to downscale to a much lower resolution for saving significantly the transmission bandwidth with negligible performance loss.