  In this paper, a learning-aided content-based wireless image transmission
scheme is proposed, where a multi-antenna-aided source wishes to securely
deliver an image to a legitimate destination in the presence of randomly
distributed 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 propose a transmission scheme, where the source employs 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 to provide security for the regions containing large amount
of information. Moreover, in order to reinforce system's security,
fountain-based packet delivery is adopted: First, the source node encodes image
packets into fountain-like packets prior to sending them over the air. The
secrecy of our proposed scheme will be achieved if the legitimate destination
correctly receives the entire image source packets, while conforming to the
latency limits of the system, before Eves can obtain the important regions.
Accordingly, the secrecy performance of our scheme is characterized by deriving
the closed-form expression for the quality-of-security (QoSec) violation
probability. Moreover, our proposed wireless image delivery scheme leverages
the deep neural network (DNN) and learns to maintain optimized transmission
parameters, while achieving a low QoSec violation probability. Simulation
results are provided with some useful engineering insights which illustrate
that our proposed learning-assisted scheme outperforms the state-of-the-arts by
achieving considerable gains in terms of security and the delay requirement.
