Multimedia Tools and Applications

, Volume 73, Issue 3, pp 1487–1506 | Cite as

Quantitative steganalysis of spatial ±1 steganography in JPEG decompressed images

  • Xing LiEmail author
  • Tao Zhang
  • Yan Zhang
  • Wenxiang Li
  • Xijian Ping


On the basis of the analysis of JPEG error and stegonoise, we propose a novel quantitative steganalyzer for spatial ±1 steganography in JPEG decompressed images. First, we present a particular theoretical argument that the cover images, which are originally stored in JPEG format, can be approximately estimated through JPEG recompression with the detected quantization table. Then, on the basis of the relationship between the message embedding rate and the variance of the stegonoise in the discrete cosine transformation (DCT) domain, we construct a polynomial regression model to estimate the secret message length. The extensive experimental results show that the proposed scheme is computationally feasible and that it significantly outperforms the existing state-of-the-art estimators, especially for the images with high quality factors and embedding rates. The order of magnitude of the prediction error using the proposed scheme can remain in the 10−4 range, as measured by the median absolute difference. Moreover, our estimator is stable and robust with respect to the embedding rate and quality factor.


Information hiding; Image Steganography Quantitative Steganalysis LSB matching JPEG decompressed images 



Discrete Cosine Transform


Least Significant Bit


Regular and Singular


The Center of the Mass of the Histogram Characteristic Function


The Center Of the Mass of the Adjacency Histogram Characteristic Function


The amplitude of local extrema


Independent identically distributed


Mean absolute error


Median absolute difference


Interquartile range



This work was supported by the National Natural Science Foundation of China (No. 60903221, No. 61272490) and the National High Technology Research and Development Program of China (“863” Program, No. 2011AA010603, No. 2011AA010605). The authors would like to thank the reviewers for their insightful comments and helpful suggestions.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Xing Li
    • 1
    Email author
  • Tao Zhang
    • 1
  • Yan Zhang
    • 2
  • Wenxiang Li
    • 1
  • Xijian Ping
    • 1
  1. 1.Zhengzhou Information Science and Technology InstituteZhengzhouChina
  2. 2.Zhengzhou University of Light IndustryZhengzhouChina

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