Semantic Communication (SemCom), leveraging its significant advantages in transmission efficiency and reliability, has emerged as a core technology for constructing future intellicise (intelligent and concise) wireless networks. However, intelligent attacks represented by semantic eavesdropping pose severe challenges to the security of SemCom. To address this challenge, Semantic Steganographic Communication (SemSteCom) achieves ``invisible'' encryption by implicitly embedding private semantic information into cover modality carriers. The state-of-the-art study has further introduced generative diffusion models to directly generate stega images without relying on original cover images, effectively enhancing steganographic capacity. Nevertheless, the recovery process of private images is highly dependent on the guidance of private semantic keys, which may be inferred by intelligent eavesdroppers, thereby introducing new security threats. To address this issue, we propose an Agentic AI-driven SemSteCom (AgentSemSteCom) scheme, which includes semantic extraction, digital token controlled reference image generation, coverless steganography, semantic codec, and optional task-oriented enhancement modules. The proposed AgentSemSteCom scheme obviates the need for both cover images and private semantic keys, thereby boosting steganographic capacity while reinforcing transmission security. The simulation results on open-source datasets verify that, AgentSemSteCom achieves better transmission quality and higher security levels than the baseline scheme.

翻译：语义通信凭借其在传输效率和可靠性方面的显著优势，已成为构建未来智能无线网络的核心技术。然而，以语义窃听为代表的智能攻击对语义通信的安全性构成了严峻挑战。为应对这一挑战，语义隐写通信通过将私有语义信息隐式嵌入载体模态中，实现了“不可见”的加密。当前最先进的研究进一步引入生成扩散模型，直接生成隐写图像而无需依赖原始载体图像，有效提升了隐写容量。然而，私有图像的恢复过程高度依赖于私有语义密钥的引导，这些密钥可能被智能窃听者推断，从而引入新的安全威胁。为解决这一问题，我们提出了一种智能体AI驱动的语义隐写通信方案，该方案包含语义提取、数字令牌控制的参考图像生成、无载体隐写、语义编解码以及可选的任务导向增强模块。所提出的AgentSemSteCom方案无需载体图像和私有语义密钥，从而在增强传输安全性的同时提升了隐写容量。在开源数据集上的仿真结果验证了AgentSemSteCom相较于基线方案，能够实现更好的传输质量和更高的安全等级。