On Learning-Assisted Content-Based Secure Image Transmission for Delay-Aware Systems With Randomly-Distributed Eavesdroppers

Abstract

In this paper, a learning-aided content-based image transmission scheme is proposed, where a multi-antenna source wishes to securely deliver an image to a legitimate destination in the presence of randomly-distributed passive eavesdroppers (Eves). We take into account the fact that not all regions of an image have the same importance from the security perspective. Hence, we employ a hybrid method to realize both the error-free data delivery of public regions—containing less-important pixels; and an artificial noise (AN)-aided transmission scheme for securing the confidential packets. To reinforce system’s security, fountain-based packet delivery is also adopted, where the source node encodes images into fountain-like packets prior to sending them over the air. The secrecy is achieved when the legitimate destination correctly receives the entire source packets before Eves obtain the important regions, while conforming to the latency limits of the system. Accordingly, the secrecy performance of our scheme is characterized by deriving a closed-form expression for the quality-of-security (QoSec) violation probability. Moreover, our proposed image delivery scheme leverages a deep neural network (DNN) and learns to maintain optimized transmission parameters, while achieving a low QoSec violation probability. Simulation results are provided to illustrate that our proposed learning-assisted scheme outperforms the state-of-the-arts by achieving considerable gains in terms of security and delay requirement.