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Open Access

High-Security HEVC Video Steganography Method Using the Motion Vector Prediction Index and Motion Vector Difference

College of Cryptography Engineering in Engineering, University of the Chinese People’s Armed Police Force, Xi’an 710086, China
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Abstract

Recently proposed steganalysis methods based on the local optimality of motion vector prediction (MVP) indicate that the existing HEVC (high efficiency video coding) motion vector (MV) domain video steganography algorithms can disturb the optimality of MVP in advanced motion vector prediction (AMVP) technology. In order to improve the security of steganography algorithm, this paper proposes an MV domain steganography method in HEVC based on MVP’s index and motion vector difference (MVD). First, we analyze the conditions that need to be met for steganography to resist attacks from MVP’s optimality features and other traditional steganalysis features. Then, a distortion function for minimizing embedding distortion is designed, and an algorithm for secret message embedding and extraction in units of inter-frame is proposed. Experimental results show that the proposed algorithm can resist attacks based on the optimality of MVP and also has high security against other traditional steganalysis methods. In addition, the proposed algorithm has excellent performance in visual quality and coding efficiency, and can be applied to practical scenarios of video covert communication.

Keywords

HEVC video steganographymotion vector prediction (MVP)advanced motion vector prediction (AMVP)local optimalityvideo steganalysis

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Tsinghua Science and Technology
Volume 30 Issue 2,
April 2025
Pages 813-829
DOI: 10.26599/TST.2024.9010016
Cite this article:
Li J, Zhang M, Niu K, et al. High-Security HEVC Video Steganography Method Using the Motion Vector Prediction Index and Motion Vector Difference. Tsinghua Science and Technology, 2025, 30(2): 813-829. https://doi.org/10.26599/TST.2024.9010016

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Received: 31 October 2023
Revised: 16 January 2024
Accepted: 16 January 2024
Published: 12 April 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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