VHNet: A Video Hiding Network with robustness to video coding?

Data hiding is an essential practice for ensuring the security of confidential data and secret communication and thus has been intensively studied. The development of deep learning enables the realization of video hiding, which aims to hide a video into another. Different from image data hiding, stego videos generated by video hiding methods need to undergo video coding in real-world scenarios because the unencoded raw videos are too large to be easily transmitted or stored. However, previous video-hiding works did not consider this, leading to a common vulnerability to information loss caused by video coding on stego videos. In other words, previous works considered videos simply as sequences of temporally continuous images, and thus the image sequences output from the data hiding network were fed directly into the recovery network to obtain secret information without video coding. We propose a module called CompressionSimulator to make stego videos robust to video coding. CompressionSimulator is pre-trained to simulate the distortion caused by video coding, and then trained together with the whole framework to make the data hiding network robust to video coding. Furthermore, an invertible neural network based framework is introduced for our video hiding task, i.e. hiding one video in another, in which we design an auxiliary module to capture the spatio-temporal relationships of videos. In general, we develop a video hiding network (VHNet) that provides video coding robustness. We conduct experiments to prove the necessity of incorporating video coding into the design of video hiding networks. Results of experiments conducted on the UCF 101 dataset show that our proposed work outperforms previous methods, and our framework achieves optimal performance in both visual and quantitative comparisons. To the best of our knowledge, we are the first paper to propose a learning-based scheme to improve the robustness of hiding videos with videos.