用深度语义隐藏技术实现视频传输中的隐秘安全通信。
SemCovert: Secure and Covert Video Transmission via Deep Semantic-Level Hiding
- 设计语义隐藏与提取双模型，嵌入秘密信息于语义层面。
- 随机化隐藏策略提升抗分析能力，隐蔽性更强。
- 保留视频质量，适合对隐私敏感的实时视频场景。
视频语义通信因传输效率高而受到关注，但仍面临隐私泄露问题。传统加密和隐写技术难以应对语义级变换与抽象，且视频的时间连续性易暴露分布异常，导致内容被重建。为此，我们提出 SemCovert，一种深度语义级隐藏框架，用于安全且隐蔽的视频传输。该框架包含一对协同设计的模型：语义隐藏模型与秘密语义提取器，无缝集成于语义通信流程中。授权接收方可可靠恢复隐藏信息，而普通用户无法感知。为进一步增强抗分析能力，引入随机化语义隐藏策略，打破嵌入确定性，引入不可预测的分布模式。实验表明，SemCovert有效降低窃听与检测风险，秘密视频传输可靠且隐蔽，视频质量仅轻微下降，保持传输保真度。结果验证了其在不损害语义通信性能的前提下实现安全隐蔽传输的有效性。
原文摘要 · Abstract (English)
Video semantic communication, praised for its transmission efficiency, still faces critical challenges related to privacy leakage. Traditional security techniques like steganography and encryption are challenging to apply since they are not inherently robust against semantic-level transformations and abstractions. Moreover, the temporal continuity of video enables framewise statistical modeling over extended periods, which increases the risk of exposing distributional anomalies and reconstructing hidden content. To address these challenges, we propose SemCovert, a deep semantic-level hiding framework for secure and covert video transmission. SemCovert introduces a pair of co-designed models, namely the semantic hiding model and the secret semantic extractor, which are seamlessly integrated into the semantic communication pipeline. This design enables authorized receivers to reliably recover hidden information, while keeping it imperceptible to regular users. To further improve resistance to analysis, we introduce a randomized semantic hiding strategy, which breaks the determinism of embedding and introduces unpredictable distribution patterns. The experimental results demonstrate that SemCovert effectively mitigates potential eavesdropping and detection risks while reliably concealing secret videos during transmission. Meanwhile, video quality suffers only minor degradation, preserving transmission fidelity. These results confirm SemCovert's effectiveness in enabling secure and covert transmission without compromising semantic communication performance.
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