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。

从隐蔽隐藏到视觉编辑：强大的生成式视频隐写术

From Covert Hiding to Visual Editing: Robust Generative Video  Steganography

Video steganography is the art of unobtrusively concealing secret data in a
cover video and then recovering the secret data through a decoding protocol at
the receiver end. Although several attempts have been made, most of them are
limited to low-capacity and fixed steganography. To rectify these weaknesses,
we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN)
in this paper. For large-capacity, we present a reversible pipeline to perform
multiple videos hiding and recovering through a single invertible neural
network (INN). Our method can hide/recover 7 secret videos in/from 1 cover
video with promising performance. For flexibility, we propose a
key-controllable scheme, enabling different receivers to recover particular
secret videos from the same cover video through specific keys. Moreover, we
further improve the flexibility by proposing a scalable strategy in multiple
videos hiding, which can hide variable numbers of secret videos in a cover
video with a single model and a single training session. Extensive experiments
demonstrate that with the significant improvement of the video steganography
performance, our proposed LF-VSN has high security, large hiding capacity, and
flexibility. The source code is available at this https URL

本文介绍了一种大容量和灵活的视频隐写网络 (LF-VSN)，提出了一种可逆的管道，可以通过单个神经网络隐藏 / 恢复多个秘密视频，还介绍了一种可控密钥方案，使不同的接收者能够从相同的覆盖视频中恢复特定的秘密视频。同时，为多个视频隐藏提供了可伸缩策略，可以通过单个模型和单个训练会话在覆盖视频中隐藏不同数量的秘密视频。经过广泛的实验证明，在视频隐写性能显著提高的同时，我们提出的 LF-VSN 具有高安全性，大隐藏容量和灵活性。