Diffusion-Driven Video Steganography: A Three-Stage Framework for Robust and High-Capacity Data Concealment

Steganography facilitates secure communication by concealing messages within digital media, with video emerging as a preferred cover medium due to its inherent high-capacity data embedding capability. Despite its potential, limitations in terms of its robustness and embedding effectiveness still hinder its application in the mainstream. To overcome the aforementioned limitations, we introduce Diffusion-Driven Video Steganography, a novel three-stage framework that synergistically integrates QR code encoding with advanced video processing techniques. In the first stage, QR code is introduced to convert secret information into images, providing assurance for the correct recovery. The second stage employs diffusion models enhanced with attention mechanisms to dynamically adapt embedding strategies, enabling seamless integration of data into video content while preserving visual fidelity. Finally, the invertibility of the Denoising Diffusion Implicit Models (DDIM) is utilized to accurately reconstruct the hidden information from the steganographic video. Extensive experiments conducted on multiple benchmark datasets validate the effectiveness of our framework, which consistently surpasses conventional video steganography methods in terms of embedding capacity, imperceptibility, and robustness to real-world perturbations.