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.

通过在视频编辑过程中将秘密信息嵌入语义特征，提出了一种创新的视频隐写术方法。引入了端到端的鲁棒性生成式视频隐写术网络（RoGVS），通过修改视频的语义特征来实现视觉编辑和嵌入秘密信息，通过人脸替换场景展示了其视觉编辑效果。实验证明，RoGVS 方法在面部视频数据集上的应用在鲁棒性和容量两方面优于现有的视频和图像隐写术技术。