将隐秘信息嵌入任务特征中，实现隐蔽语义通信的双路径自适应框架。
Adaptive Dual-Path Framework for Covert Semantic Communication
- 双路径架构：显式路径处理公开任务，隐写路径融合公密信息进行语义编码。
- 自适应选择机制动态激活网络模块，兼顾任务性能与隐蔽性。
- 在Cityscapes数据集上检测准确率降至56.12%，接近随机猜测水平。
本文提出一种新型自适应双路径框架用于隐蔽语义通信（SemCom），将隐蔽信息传输与面向任务的语义编码结合。不同于传统通过功率域信号叠加隐藏消息的方法，本框架通过语义级内在编码将隐蔽数据嵌入特定任务特征中。新架构引入双编码路径与自适应块选择：显式路径用于公共任务执行，隐写路径通过对比表示对齐联合编码公共与隐蔽信息。基于Gumbel-Softmax的自适应路径选择机制根据任务需求动态激活网络模块。我们构建了多目标优化框架，同时保障语义理解准确性与隐蔽传输可靠性。在独立训练攻击者下的严格安全评估表明，本方法在Cityscapes数据集上将攻击者检测准确率压制至接近随机猜测的56.12%。该强安全性同时保持了优于基线的主任务性能。
原文摘要 · Abstract (English)
This paper proposes a novel adaptive dual-path framework for covert semantic communication (SemCom), which integrates covert information transmission with task-oriented semantic coding. Unlike conventional covert communication methods that embed hidden messages through power-domain signal superposition, our framework embeds covert data within task-specific features via semantic-level intrinsic encoding. This new architecture introduces dual encoding paths with adaptive block selection: an Explicit path for public task execution and a Stego path that jointly encodes both public and covert information through contrastive representation alignment. A Gumbel-Softmax enabled adaptive path selection mechanism dynamically activates network blocks based on task require- ments. We formulate a multi-objective optimization framework that simultaneously ensures accurate semantic understanding and reliable covert transmission. We rigorously evaluate our framework's security against a powerful, independently trained attacker. Experimental results on the Cityscapes dataset demon- strate a state-of-the-art level of covertness: our method suppresses the attacker's detection accuracy to a near-random guessing level of 56.12%. This robust security is achieved while simultaneously maintaining superior performance on the primary semantic tasks compared to the baselines.
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