这篇论文点出了中继语义通信里一个隐蔽的隐私漏洞，用对抗训练把合法和窃听者的语义差距拉大，做法很直接有效。
论文研究 relay-assisted 语义通信系统的隐私问题，指出中继节点即使无源数据也能可靠推断语义并重构信号，其性能与合法接收器相当。提出迭代对抗训练框架，通过交替优化中继窃听与合法系统，在保持合法接收器解码性能的同时显著抑制中继推理。合法与窃听端语义精度差距在多种信道条件下扩大，实现隐蔽隐私保护。方法基于高保真重构与选择性语义抑制的平衡。
Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications
Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw data, enabling efficient and robust communication under resource and channel constraints. In this paper, the privacy implications of relay-assisted SemCom systems are studied, where the intermediate relay node operates directly on learned latent representations. It is shown that the relay, even without access to source data, can reliably infer semantic meaning and reconstruct signals with performance comparable to that of the legitimate receiver, revealing a fundamental privacy vulnerability of semantic representations. To address this issue, an iterative adversarial training framework is proposed in which a strong, adaptively trained eavesdropper at the relay is explicitly accounted for. The proposed approach alternates between optimizing the relay's eavesdropping function and the legitimate system, resulting in representations that preserve semantic decoding performance at the intended receiver while degrading semantic inference at the relay. The semantic accuracy gap between the legitimate receiver and the eavesdropper is significantly enlarged across channel conditions. Importantly, this protection is achieved in a stealthy manner, with high reconstruction fidelity maintained while semantic leakage is selectively suppressed.