A One-dimensional HEVC video steganalysis method using the Optimality of Predicted Motion Vectors

TL;DR

This paper introduces a novel HEVC video steganalysis method based on the optimality of predicted motion vectors, effectively distinguishing cover videos from stego videos with high accuracy and low complexity.

Contribution

It proposes a new steganalysis feature based on the optimality of predicted motion vectors in HEVC, improving detection performance without requiring model training.

Findings

Achieves 100% optimal MVP rate in cover videos

Stego videos show less than 100% optimal MVP rate

Outperforms four state-of-the-art steganalysis methods

Abstract

Among steganalysis techniques, detection against motion vector (MV) domain-based video steganography in High Efficiency Video Coding (HEVC) standard remains a hot and challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis feature based on the optimality of predicted MVs with a dimension of one. Firstly, we point out that the motion vector prediction (MVP) of the prediction unit (PU) encoded using the Advanced Motion Vector Prediction (AMVP) technique satisfies the local optimality in the cover video. Secondly, we analyze that in HEVC video, message embedding either using MVP index or motion vector differences (MVD) may destroy the above optimality of MVP. And then, we define the optimal rate of MVP in HEVC video as a steganalysis feature. Finally, we conduct steganalysis detection experiments on two general datasets for three popular steganography methods and compare the performance with four state-of-the-art steganalysis methods. The experimental results show that the proposed optimal rate of MVP for all cover videos is 100\%, while the optimal rate of MVP for all stego videos is less than 100\%. Therefore, the proposed steganography scheme can accurately distinguish between cover videos and stego videos, and it is efficiently applied to practical scenarios with no model training and low computational complexity.