Secure Semantic Image Transmission over Wiretap Channels

Existing semantic communications have exhibited satisfactory performance in many tasks, but secure image transmission has not been adequately investigated. In this paper, we propose a novel secure semantic image transmission (SSIT) framework over multiple-input single-output (MISO) wiretap channels. To enhance image transmission security for a legitimate semantic user (SU) while interfering with the eavesdropper (Eve), a type of beneficial semantic noise map, which is determined by both source and SU channel states, is produced by a semantic noise-aided conditional variational autoencoder (SN-CVAE) in an unsupervised manner. Furthermore, to improve the secure image reconstruction quality, we propose an efficient transmit beamformer optimization algorithm and leverage the constrained stochastic successive convex approximation (CSSCA) to solve the optimization problem. Numerical results demonstrate that our method effectively protects image information from eavesdroppers while ensuring high-fidelity image reconstruction at the legitimate receiver.