From Covert Hiding to Visual Editing: Robust Generative Video
  Steganography
Xueying Mao Xiaoxiao Hu Wanli Peng Zhenliang Gan Qichao Ying Zhenxing Qian Sheng Li Xinpeng Zhang
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.