Deep Joint Encryption and Source-Channel Coding: An Image Privacy Protection Approach

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.