From Covert Hiding to Visual Editing: Robust Generative Video Steganography
This work addresses the need for more robust steganography in video editing for applications like secure communication, though it appears incremental by focusing on a specific scenario like face-swapping.
The paper tackles the problem of video steganography lacking robustness against distortions in online social networks by proposing an end-to-end network that embeds secret messages into semantic features during video editing, achieving superior robustness and capacity compared to existing methods.
Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic feature for steganography during the video editing process. Although existing traditional video steganography methods display a certain level of security and embedding capacity, they lack adequate robustness against common distortions in online social networks (OSNs). In this paper, we introduce an end-to-end robust generative video steganography network (RoGVS), which achieves visual editing by modifying semantic feature of videos to embed secret message. We employ face-swapping scenario to showcase the visual editing effects. We first design a secret message embedding module to adaptively hide secret message into the semantic feature of videos. Extensive experiments display that the proposed RoGVS method applied to facial video datasets demonstrate its superiority over existing video and image steganography techniques in terms of both robustness and capacity.