Robust coverless video steganography based on pose estimation and object tracking

Existing coverless video steganography methods have not adequately exploited the stable features within and between video frames, and they have neglected the subtlety required for carrier transmission. To address these issues, this paper proposes a coverless video steganography method based on pose estimation and object tracking. By analyzing the intra-frame and inter-frame features of human posture within videos, this method hides secret information in videos depicting human activities, thereby enhancing concealment through simulating social behaviors. The scheme initially utilizes pose estimation network to localize target persons and their respective pose keypoints. Subsequently, a multi-object tracking algorithm is employed to track the detected targets within the video, coupled with a filtering mechanism to identify and prioritize tracking targets with larger areas, thus ensuring robustness in the tracking process. Then, corresponding hash mapping rules are established based on the inter-frame movement direction and the intra-frame angle features of the tracking targets. Finally, an inverted index is constructed to accelerate the speed of matching carrier videos containing the secret information and complete information hiding. Experimental results demonstrate that the proposed approach exhibits superior robustness against a variety of traditional attacks, video compression attacks, and frame dropping attacks compared to latest methods, while also enhancing the hiding capacity.