Semantic Noise Aided Secure Image Transmission over MIMO Fading Channels

2026-06-29

AI Summary

This work addresses the challenge in existing semantic communication systems of simultaneously achieving high-fidelity image reconstruction for legitimate users and effective protection against eavesdroppers. To this end, we propose a secure image semantic communication framework tailored for MIMO fading channels, which innovatively integrates a semantic noise mechanism informed by both source features and the legitimate user’s channel state, along with jointly optimized transmit and receive beamforming. The design synergistically combines a semantic noise generation network, a channel estimation enhancement network, and a constrained stochastic successive convex approximation algorithm. This integrated approach ensures high-quality image recovery at the intended receiver while significantly degrading the eavesdropper’s ability to extract meaningful information. Experimental results demonstrate the dual advantages of the proposed scheme in both reconstruction fidelity and communication security.

Abstract

Existing semantic communications have exhibited satisfactory performance in many tasks, but secure image transmission remains insufficiently explored. We propose a novel secure image semantic communication (SISC) framework over multiple-input multiple-output (MIMO) fading channels. To ensure high-quality image reconstruction for the legitimate semantic user (SU) and simultaneously interfere with the eavesdropper (Eve), we design a semantic noise generation (SNG) network. This network generates a beneficial semantic noise map based on both the source features and the SU channel state information (CSI). An efficient channel estimation enhanced network is incorporated to obtain the accurate CSI and enhance the system performance. Furthermore, to improve the secure image reconstruction quality, we develop an efficient transceiver beamformer optimization algorithm, where the formulated problem is solved using the constrained stochastic successive convex approximation method. In the proposed SISC framework, semantic noise generation and beamforming optimization work together to ensure secure and high-quality image transmission. Numerical results demonstrate that the proposed semantic noise aided transmission scheme effectively protects image information from leakage to Eve while maintaining high-fidelity image reconstruction at SU.

Problem

• secure image transmission
• semantic communication
• MIMO fading channels
• eavesdropping
• image reconstruction

Innovation

• Semantic Noise Generation
• Secure Semantic Communication
• MIMO Fading Channels
• Beamformer Optimization
• Channel State Information