SemCovert Framework Introduces Semantic-Level Hiding for Secure Video Transmission

Global: SemCovert Framework Introduces Semantic-Level Hiding for Secure Video Transmission

A preprint posted on arXiv (ID 2512.22233) presents SemCovert, a deep learning framework designed to embed secret video content within semantic communication streams while preserving the appearance of standard video for unauthenticated viewers. The authors propose the system as a response to identified privacy risks associated with the transmission of semantically encoded video data.

Background on Video Semantic Communication

Video semantic communication leverages high‑level feature extraction to reduce bandwidth usage, yet the process can expose sensitive information through statistical patterns that persist across frames. Conventional protection methods such as steganography and encryption are noted to be less effective when applied to transformed semantic representations.

System Architecture

SemCovert comprises two jointly trained components: a semantic hiding model that integrates covert payloads into the semantic encoding pipeline, and a secret semantic extractor that enables authorized receivers to retrieve the concealed video. The design intends to keep the embedded data imperceptible to regular users of the communication system.

Randomized Embedding Strategy

To reduce predictability, the framework incorporates a randomized hiding strategy that varies embedding patterns across frames, thereby disrupting deterministic statistical signatures that could be exploited for detection.

Experimental Findings

According to the abstract, experimental evaluation indicated that the approach reduces the likelihood of successful eavesdropping and detection while maintaining video quality with only slight degradation. The reported metrics suggest that the concealed video can be recovered reliably by the intended extractor.

Implications and Future Directions

The authors suggest that SemCovert may enable covert transmission of video content without compromising the efficiency benefits of semantic communication. Further validation on larger datasets and in real‑world network conditions is implied as a next step.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.