Research on Video Steganography Based on Optical Flow Perception and Discrete Wavelet Transform

The goal of video steganography is to embed secret information into video content in a way that it remains imperceptible, while ensuring that the embedded information can be reliably extracted by the receiver. Owing to their large data capacity and rich temporal dynamics, videos offer strong potential for covert information embedding. However, the complex structure of video data and the high compression imposed by modern video encoding algorithms pose significant challenges. Existing deep learning-based video steganography frameworks often struggle to maintain robustness under such compression. In this work, we propose an end-to-end video steganography model based on optical flow guidance and discrete wavelet transform (DWT). The model supports high-capacity embedding while preserving visual quality under video compression and transcoding. It consists of an encoder and a decoder, where the encoder leverages optical flow maps to capture temporal motion information and performs fine-grained embedding in the frequency domain via DWT. A distortion simulation layer is integrated into the network to mimic real-world degradation scenarios such as video compression attacks. Experimental results demonstrate that the proposed method achieves high embedding capacity and accurate information retrieval, while maintaining superior visual quality. Moreover, it exhibits strong robustness against video compression, highlighting its potential for practical steganographic applications.