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Anti-JPEG Compression Steganography Based on the High Tense Region Locating Method

by Yang Wu1, Weiping Shang2, Jiahao Chen3

State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450002, China.
School of Telecommunications Engineering, Xidian University, Xi’an, 710071, China.
Zhuhai Campus of Beijing Normal University, Zhuhai, 519000, China.

* Corresponding Author: Weiping Shang. Email: email.

Computers, Materials & Continua 2019, 59(1), 199-214. https://doi.org/10.32604/cmc.2019.05194

Abstract

Robust data hiding techniques attempt to construct covert communication in a lossy public channel. Nowadays, the existing robust JPEG steganographic algorithms cannot overcome the side-information missing situation. Thus, this paper proposes a new robust JPEG steganographic algorithm based on the high tense region location method which needs no side-information of lossy channel. First, a tense region locating method is proposed based on the Harris-Laplacian feature point. Then, robust cover object generating processes are described. Last, the advanced embedding cost function is proposed. A series of experiments are conducted on various JPEG image sets and the results show that the proposed steganographic algorithm can resist JPEG compression efficiently with acceptable performance against steganalysis statistical detection libraries GFR (Gabor Filters Rich model) and DCTR (Discrete Cosine Transform Residual).

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Cite This Article

APA Style
Wu, Y., Shang, W., Chen, J. (2019). Anti-jpeg compression steganography based on the high tense region locating method. Computers, Materials & Continua, 59(1), 199-214. https://doi.org/10.32604/cmc.2019.05194
Vancouver Style
Wu Y, Shang W, Chen J. Anti-jpeg compression steganography based on the high tense region locating method. Comput Mater Contin. 2019;59(1):199-214 https://doi.org/10.32604/cmc.2019.05194
IEEE Style
Y. Wu, W. Shang, and J. Chen, “Anti-JPEG Compression Steganography Based on the High Tense Region Locating Method,” Comput. Mater. Contin., vol. 59, no. 1, pp. 199-214, 2019. https://doi.org/10.32604/cmc.2019.05194



cc Copyright © 2019 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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