Secure Transmission in Wireless Semantic Communications With Adversarial Training

Abstract

The burgeoning technology of deep learning-based semantic communications has significantly enhanced the efficiency and reliability of wireless communication systems by facilitating the transmission of semantic features. However, security threats, notably the interception of sensitive data, remain a significant challenge for secure communications. To safeguard the confidentiality of transmitted semantics and effectively counteract eavesdropping threats, this letter proposes a secure deep learning-based semantic communication system, SecureDSC. It comprises semantic encoder/decoder, channel encoder/decoder, and encryption/decryption modules with a key processing network. By incorporating a symmetric encryption module and an attacker-oriented adversarial network, SecureDSC guarantees the secure transmission between legitimate users in the semantic communications. Besides, experiments are conducted to evaluate the effectiveness and feasibility of the proposed scheme.