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.

翻译：视频语义通信虽因其传输效率备受赞誉，但仍面临隐私泄露的关键挑战。传统安全技术（如隐写术与加密）难以直接应用，因其本质上难以抵抗语义层面的变换与抽象。此外，视频的时间连续性使得能够在较长时间内进行逐帧统计建模，这增加了暴露分布异常和重建隐藏内容的风险。为应对这些挑战，我们提出SemCovert——一种用于安全隐蔽视频传输的深度语义隐藏框架。SemCovert引入了一对协同设计的模型，即语义隐藏模型与秘密语义提取器，它们被无缝集成到语义通信流程中。该设计使授权接收方能可靠地恢复隐藏信息，同时确保其对普通用户不可感知。为进一步提升抗分析能力，我们提出一种随机化语义隐藏策略，该策略打破了嵌入过程的确定性，并引入不可预测的分布模式。实验结果表明，SemCovert能有效缓解潜在的窃听与检测风险，同时在传输过程中可靠地隐藏秘密视频。此外，视频质量仅受到轻微影响，保持了传输保真度。这些结果证实了SemCovert在实现安全隐蔽传输的同时，不会损害语义通信性能的有效性。