A One-dimensional HEVC video steganalysis method using the Optimality of
  Predicted Motion Vectors
Jun Li Minqing Zhang Ke Niu Yingnan Zhang Xiaoyuan Yang
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.