Joint source and channel coding (JSCC) has achieved great success due to the
introduction of deep learning. Compared with traditional separate source
channel coding (SSCC) schemes, the advantages of DL based JSCC (DJSCC) include
high spectrum efficiency, high reconstruction quality, and the relief of "cliff
effect". However, it is difficult to couple encryption-decryption mechanisms
with DJSCC in contrast with traditional SSCC schemes, which hinders the
practical usage of the emerging technology. To this end, our paper proposes a
novel method called DL based joint encryption and source-channel coding
(DJESCC) for images that can successfully protect the visual information of the
plain image without significantly sacrificing image reconstruction performance.
The idea of the design is using a neural network to conduct image encryption,
which converts the plain image to a visually protected one with the
consideration of its interaction with DJSCC. During the training stage, the
proposed DJESCC method learns: 1) deep neural networks for image encryption and
image decryption, and 2) an effective DJSCC network for image transmission in
encrypted domain. Compared with the perceptual image encryption methods with
DJSCC transmission, the DJESCC method achieves much better reconstruction
performance and is more robust to ciphertext-only attacks.Comment: 12 pages, 13 figure