Learning-task oriented semantic communication is pivotal in optimizing transmission efficiency by extracting and conveying essential semantics tailored to specific tasks, such as image reconstruction and classification. Nevertheless, the challenge of eavesdropping poses a formidable threat to semantic privacy due to the open nature of wireless communications. In this paper, intelligent reflective surface (IRS)-enhanced secure semantic communication (IRS-SSC) is proposed to guarantee the physical layer security from a task-oriented semantic perspective. Specifically, a multi-layer codebook is exploited to discretize continuous semantic features and describe semantics with different numbers of bits, thereby meeting the need for hierarchical semantic representation and further enhancing the transmission efficiency. Novel semantic security metrics, i.e., secure semantic rate (S-SR) and secure semantic spectrum efficiency (S-SSE), are defined to map the task-oriented security requirements at the application layer into the physical layer. To achieve artificial intelligence (AI)-native secure communication, we propose a noise disturbance enhanced hybrid deep reinforcement learning (NdeHDRL)-based resource allocation scheme. This scheme dynamically maximizes the S-SSE by jointly optimizing the bits for semantic representations, reflective coefficients of the IRS, and the subchannel assignment. Moreover, we propose a novel semantic context awared state space (SCA-SS) to fusion the high-dimensional semantic space and the observable system state space, which enables the agent to perceive semantic context and solves the dimensional catastrophe problem. Simulation results demonstrate the efficiency of our proposed schemes in both enhancing the security performance and the S-SSE compared to several benchmark schemes.

翻译：面向学习任务的语义通信通过提取和传递针对特定任务（如图像重建与分类）的关键语义，在优化传输效率方面至关重要。然而，由于无线通信的开放性，窃听问题对语义隐私构成了严峻威胁。本文提出智能反射面（IRS）增强的安全语义通信（IRS-SSC），从面向任务的语义视角保障物理层安全。具体而言，利用多层码本对连续语义特征进行离散化，并以不同比特数描述语义，从而满足层次化语义表示的需求并进一步提升传输效率。定义了新颖的语义安全度量指标，即安全语义速率（S-SR）与安全语义频谱效率（S-SSE），将应用层的面向任务安全需求映射至物理层。为实现人工智能（AI）原生的安全通信，我们提出一种基于噪声扰动增强混合深度强化学习（NdeHDRL）的资源分配方案。该方案通过联合优化语义表示比特数、IRS反射系数及子信道分配，动态最大化S-SSE。此外，我们提出一种新颖的语义上下文感知状态空间（SCA-SS），融合高维语义空间与可观测系统状态空间，使智能体能感知语义上下文并解决维度灾难问题。仿真结果表明，与多种基准方案相比，所提方案在提升安全性能与S-SSE方面均具有显著优势。