Secure Intellicise Wireless Network: Agentic AI for Coverless Semantic Steganography Communication

2026-01-23

AI Summary

This work addresses the vulnerability of semantic communication to intelligent eavesdropping, where existing steganographic approaches relying on private semantic keys risk key inference. To overcome this limitation, the paper proposes a novel Agentic AI–based semantic steganographic communication framework that integrates semantic extraction, digital token–controlled reference image generation, and carrier-free steganography. Notably, this approach achieves secure communication without requiring either the original cover image or a private semantic key—a first in the field. The method substantially enhances both steganographic capacity and transmission security, demonstrating superior communication quality and security performance over baseline schemes on open-source datasets.

Technology Category

• Multiagent Systems: Agent Communication
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Semantic Communication
• Semantic Steganography
• Security Threat
• Semantic Eavesdropping
• Private Semantic Key

Innovation

• Agentic AI
• Coverless Steganography
• Semantic Communication
• Semantic Steganography
• Diffusion Models