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Defining Embedding Distortion for Intra Prediction Mode-Based Video Steganography

by Qiankai Nie1, ba Xu1, Bingwen Feng1, Leo Yu Zhang2

College of Information Science and Technology, Jinan University, Huangpu Road West, No. 230, Guangzhou, 510006, China.
School of Information Technology, Deakin University, Victoria 3216, Australia.

* Corresponding author: Bingwen Feng, Email: email.

Computers, Materials & Continua 2018, 55(1), 59-70. https://doi.org/10.3970/cmc.2018.055.059

Abstract

In this paper, an effective intra prediction mode-based video strganography is proposed. Secret messages are embedded during the intra prediction of the video encoding without causing large embedding impact. The influence on the sum of absolute difference (SAD) in intra prediction modes (IPMs) reversion phenomenon is sharp when modifying IPMs. It inspires us to take the SAD prediction deviation (SPD) to define the distortion function. What is more, the mapping rule between IPMs and the codewords is introduced to further reduce the SPD values of each intra block. Syndrome-trellis code (STC) is used as the practical embedding implementation. Experimental results demonstrate that our proposed steganographic scheme presents high undetectability compared with existing IPMs-based steganographic approaches. It also outperforms these schemes on stego video quality.

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Cite This Article

APA Style
Nie, Q., Xu, X., Feng, B., Zhang, L.Y. (2018). Defining embedding distortion for intra prediction mode-based video steganography. Computers, Materials & Continua, 55(1), 59-70. https://doi.org/10.3970/cmc.2018.055.059
Vancouver Style
Nie Q, Xu X, Feng B, Zhang LY. Defining embedding distortion for intra prediction mode-based video steganography. Comput Mater Contin. 2018;55(1):59-70 https://doi.org/10.3970/cmc.2018.055.059
IEEE Style
Q. Nie, X. Xu, B. Feng, and L. Y. Zhang, “Defining Embedding Distortion for Intra Prediction Mode-Based Video Steganography,” Comput. Mater. Contin., vol. 55, no. 1, pp. 59-70, 2018. https://doi.org/10.3970/cmc.2018.055.059



cc Copyright © 2018 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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