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 https://github.com/MC-E/LF-VSN.

翻译：视频隐写是一种将秘密数据隐蔽地嵌入载体视频，并通过接收端的解码协议恢复秘密数据的艺术。尽管已有多种尝试，但现有方法大多局限于低容量和固定模式的隐写。为解决这些不足，本文提出了一种大容量与灵活视频隐写网络（LF-VSN）。针对大容量需求，我们设计了一个可逆流水线，通过单一可逆神经网络（INN）实现多视频的隐藏与恢复。该方法可在1个载体视频中隐藏/恢复7个秘密视频并取得优异性能。在灵活性方面，我们提出了一种密钥可控机制，使不同接收者能够通过特定密钥从同一载体视频中恢复指定的秘密视频。此外，我们进一步通过提出多视频隐藏的可扩展策略提升灵活性，该策略允许使用单一模型和单次训练，在载体视频中隐藏可变数量的秘密视频。大量实验表明，所提出的LF-VSN在显著提升视频隐写性能的同时，具备高安全性、大隐藏容量和灵活性。源代码已开源至 https://github.com/MC-E/LF-VSN。