  Joint source-channel coding (JSCC) has achieved great success due to the
introduction of deep learning (DL). Compared to traditional separate
source-channel coding (SSCC) schemes, the advantages of DL-based JSCC (DJSCC)
include high spectrum efficiency, high reconstruction quality, and relief of
"cliff effect". However, it is difficult to couple existing secure
communication mechanisms (e.g., encryption-decryption mechanism) with DJSCC in
contrast with traditional SSCC schemes, which hinders the practical usage of
this emerging technology. To this end, our paper proposes a novel method called
DL-based joint protection and source-channel coding (DJPSCC) for images that
can successfully protect the visual content of the plain image without
significantly sacrificing image reconstruction performance. The idea of the
design is to use a neural network to conduct visual protection, which converts
the plain image to a visually protected one with the consideration of its
interaction with DJSCC. During the training stage, the proposed DJPSCC method
learns: 1) deep neural networks for image protection and image deprotection,
and 2) an effective DJSCC network for image transmission in the protected
domain. Compared to existing source protection methods applied with DJSCC
transmission, the DJPSCC method achieves much better reconstruction
performance.
