Abstract:

The article proposed a novel reversible neural network-based video steganography model. By replacing the widely used dense connection functions in current reversible neural networks with an optimized non-local mechanism function, the model effectively addressed the issue of low-quality secret information extraction from video carriers. Through the design of a superposition-based encryption and decryption structure, the model’s security was enhanced. Additionally, by optimizing the number of reversible neural network blocks, the network efficiency was improved. Experimental results demonstrate that, when utilizing reversible neural networks for video steganography, compared to dense network structures, the reversible neural network employing the nonlocal mechanism recovers secret information of higher quality, and the video distortion after secret embedding is reduced. Furthermore, the use of the superposition-based encryption and decryption structure significantly enhances the security of reversible neural network-based video steganography at the application level.

Key words: video steganography, reversible neural network, nonlocal mechanism