Robust coverless video steganography based on inter-frame keypoint matching

Abstract

Existing coverless video steganography schemes primarily focus on single-frame features, which manifest poor robustness. To enhance resistance against adversarial attacks, this paper proposes a coverless video steganography method based on inter-frame keypoint matching, leveraging the movement orientation of keypoints within the video for information hiding. Initially, the method divides video frames into blocks, and then constructs an inter-frame keypoint matching network by using the keypoint detection method combined with deep learning. By narrowing down the matching scope and filtering out mismatches, the network effectively discerns the keypoint matching relationships. For keypoints successfully matched within the blocks, a coordinate system is established based on the original keypoint, determining the position of the matched keypoints. The most frequently occurring orientation within the blocks is designated as the orientation of the block. Subsequently, we introduce a mapping rule based on quadrant labels, generating robust hash sequences according to the quadrant labels corresponding to the orientation of the blocks. Compared to the latest coverless video steganography schemes, this method shows superior robustness in resisting 14 common traditional attacks and video attacks. Concurrently, it exhibits commendable hiding capacity and hiding success rate.