High-Capacity Convolutional Video Steganography with Temporal Residual Modeling

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