Coverless Video Steganography Based on Frame Sequence Perceptual Distance Mapping

Most existing coverless video steganography algorithms use a particular video frame for information hiding. These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness. We propose a coverless video steganography method based on frame sequence perceptual distance mapping. In this method, we introduce Learned Perceptual Image Patch Similarity (LPIPS) to quantify the similarity between consecutive video frames to obtain the sequential features of the video. Then we establish the relationship map between features and the hash sequence for information hiding. In addition, the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process. Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks. Compared with the existing methods, the robustness to Gaussian noise and speckle noise is improved by more than 40%, and the algorithm has better practicability and feasibility.