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Open Access

ARTICLE

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Jun Li1,2, Minqing Zhang1,2,*, Ke Niu1, Yingnan Zhang1, Xiaoyuan Yang1,2
1 College of Cryptography Engineering, Engineering University of the Chinese People’s Armed Police Force, Xi’an, 710086, China
2 Key Laboratory of Network and Information Security of the Chinese People’s Armed Police Force, Xi’an, 710086, China
* Corresponding Author: Minqing Zhang. Email: email
(This article belongs to the Special Issue: Multimedia Encryption and Information Security)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.048095

Received 27 November 2023; Accepted 18 March 2024; Published online 24 April 2024

Abstract

Among steganalysis techniques, detection against MV (motion vector) domain-based video steganography in the HEVC (High Efficiency Video Coding) standard remains a challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis method that can perfectly detect MV-based steganography in HEVC. Firstly, we define the local optimality of MVP (Motion Vector Prediction) based on the technology of AMVP (Advanced Motion Vector Prediction). Secondly, we analyze that in HEVC video, message embedding either using MVP index or MVD (Motion Vector Difference) may destroy the above optimality of MVP. And then, we define the optimal rate of MVP 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 demonstrate the effectiveness of the proposed feature set. Furthermore, our method stands out for its practical applicability, requiring no model training and exhibiting low computational complexity, making it a viable solution for real-world scenarios.

Keywords

Video steganography; video steganalysis; motion vector prediction; motion vector difference; advanced motion vector prediction; local optimality

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