High-Capacity Convolutional Video Steganography with Temporal Residual Modeling

Steganography represents the art of unobtrusively concealing a secret message within some cover data. The key scope of this work is about high-capacity visual steganography techniques that hide a full-sized color video within another. We empirically validate that high-capacity image steganography model doesn't naturally extend to the video case for it completely ignores the temporal redundancy within consecutive video frames. Our work proposes a novel solution to this problem(i.e., hiding a video into another video). The technical contributions are two-fold: first, motivated by the fact that the residual between two consecutive frames is highly-sparse, we propose to explicitly consider inter-frame residuals. Specifically, our model contains two branches, one of which is specially designed for hiding inter-frame residual into a cover video frame and the other hides the original secret frame. And then two decoders are devised, revealing residual or frame respectively. Secondly, we develop the model based on deep convolutional neural networks, which is the first of its kind in the literature of video steganography. In experiments, comprehensive evaluations are conducted to compare our model with classic steganography methods and pure high-capacity image steganography models. All results strongly suggest that the proposed model enjoys advantages over previous methods. We also carefully investigate our model's security to steganalyzer and the robustness to video compression.