Survey on Intelligent Semantic Covert Communication

FENG Zhaoxin; XU Yifan; XING Chengwen; XU Yuhua; ZHAO Nan; WANG Jinlong

doi:10.11999/JEIT260184

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FENG Zhaoxin, XU Yifan, XING Chengwen, XU Yuhua, ZHAO Nan, WANG Jinlong. Survey on Intelligent Semantic Covert Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260184

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FENG Zhaoxin, XU Yifan, XING Chengwen, XU Yuhua, ZHAO Nan, WANG Jinlong. Survey on Intelligent Semantic Covert Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260184

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Survey on Intelligent Semantic Covert Communication

doi: 10.11999/JEIT260184 cstr: 32379.14.JEIT260184

1.
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
2.
College of Communications Engineering, Army Engineering University of PLA, Nanjing 210000, China
3.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: The National Natural Science Foundation of China (U23A20271, 62325103, 62271099)

Received Date: 2026-02-11
Accepted Date: 2026-05-29
Rev Recd Date: 2026-05-19

Available Online: 2026-06-10

Abstract

Abstract

Significance As the Sixth-Generation mobile communication network (6G) evolves from the Internet of Everything to the Intelligent Internet of Everything, the communication paradigm is shifting from reliable bit transmission to effective semantic transmission. Semantic communication extracts and compresses task-related semantics to reduce redundancy and resource use. However, because semantic information is highly structured and task-specific, it is vulnerable to eavesdropping, inference, and attacks. Covert communication addresses this risk by hiding transmission behavior from unauthorized monitoring. With support from Artificial Intelligence (AI), covert communication can use reinforcement learning to adjust power and resource allocation in dynamic environments. Generative models can also conceal transmitted signals by learning and reproducing environmental patterns. However, strict covertness constraints limit the achievable transmission rate and make large-scale information transmission difficult. Intelligent semantic covert communication integrates semantic extraction with covert transmission, providing a reliable approach to secure and efficient 6G communications. Progress With the development of AI, especially deep learning for complex feature modeling, semantic communication can support efficient semantic extraction and nonlinear compression of multimodal data. Research on semantic communication has also shifted from Separate Source-Channel Coding (SSCC) to Joint Source-Channel Coding (JSCC), which supports end-to-end training and improved transmission performance. For image transmission, Convolutional Neural Networks (CNNs) use local receptive fields to capture spatial correlations. For sequential data transmission, Long Short-Term Memory (LSTM) networks use gating mechanisms to maintain temporal coherence. In covert communication, Generative Adversarial Networks (GANs) and diffusion models can learn the statistical patterns of environmental noise in the time, frequency, and spatial domains, thereby concealing transmitted signals. These methods reduce the effectiveness of unauthorized monitoring and detection, and improve system adaptability in dynamic environments. AI also improves autonomous decision-making in dynamic covert communication. By modeling covert transmission as a Markov Decision Process (MDP), Deep Reinforcement Learning (DRL) can learn resource allocation strategies through interaction with the environment. This approach reduces computational complexity compared with traditional convex optimization methods. By integrating semantic extraction and covert transmission, intelligent semantic covert communication further supports semantic-driven covert transmission. Large Language Models (LLMs) can evaluate semantic sensitivity and contextual risks, enabling selective covert transmission of sensitive semantic information. Conclusions Research on intelligent semantic covert communication shows the advantages of coordinated semantic perception and physical-layer covert mechanisms. AI improves semantic extraction efficiency and strengthens adaptation to dynamic and complex environments. By integrating semantic understanding with covert transmission strategies, intelligent semantic covert communication supports both efficiency and security for ubiquitous 6G services. Prospects Future research on intelligent semantic covert communication should address several key challenges, including AI-enabled detection, unified semantic metrics, lightweight model design, multimodal semantic alignment, system interpretability, and semantic hallucination. Active threat detection and adaptive defense strategies are needed to counter AI-driven surveillance. Causal reasoning in Large Multimodal Models (LMMs) can help mitigate semantic hallucination and improve data transmission reliability. Advances in model compression and cloud-edge collaboration are also needed to deploy high-complexity AI models on resource-limited terminals. With the rapid development of AI, intelligent semantic covert communication is expected to provide core support for intelligent connectivity of everything and help build more secure, efficient, and reliable 6G networks.
- Sixth-Generation mobile communication (6G),
- Artificial Intelligence (AI),
- Semantic covert communication,
- Semantic communication,
- Covert communication

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References(43)

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