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  • Research Article
  • Open Access

Improved Steganographic Method Preserving Pixel-Value Differencing Histogram with Modulus Function

EURASIP Journal on Advances in Signal Processing20102010:249826

Received: 20 August 2009

Accepted: 20 March 2010

Published: 29 April 2010


We herein advance a secure steganographic algorithm that uses a turnover policy and a novel adjusting process. Although the method of Wang et al. uses Pixel-Value Differencing (PVD) and their modulus function provides high capacity and good image quality, the embedding process causes a number of artifacts, such as abnormal increases and fluctuations in the PVD histogram, which may reveal the existence of the hidden message. In order to enhance the security of the algorithm, a turnover policy is used that prevents abnormal increases in the histogram values and a novel adjusting process is devised to remove the fluctuations at the border of the subrange in the PVD histogram. The proposed method therefore eliminates all the weaknesses of the PVD steganographic methods thus far proposed and guarantees secure communication. In the experiments described herein, the proposed algorithm is compared with other PVD steganographic algorithms by using well-known steganalysis techniques, such as RS-analysis, steganalysis for LSB matching, and histogram-based attacks. The results support our contention that the proposed method enhances security by keeping the PVD histogram similar to the cover, while also providing high embedding capacity and good imperceptibility to the naked eye.


  • Good Image
  • Secure Communication
  • Full Article
  • Adjust Process
  • Modulus Function

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Authors’ Affiliations

Department of Computer Science, Korea Advanced Institute of Science and Technology, Yuseong-Gu, Daejeon, Republic of Korea
School of Computer and Software Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, Republic of Korea


© Jeong-Chun Joo et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.