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.