Share Email Print

Journal of Electronic Imaging

Steganalysis of least significant bit matching based on image histogram and correlation
Author(s): Zhihua Xia; Shufang Wang; Xingming Sun; Baowei Wang
Format Member Price Non-Member Price
PDF $20.00 $25.00

Paper Abstract

The steganalysis/detection of spatial domain least significant bit (LSB) matching steganography in grayscale images, which is the antetype of many sophisticated steganographic methods, is concentrated on. Spatial LSB matching can be modeled by adding independent noise to the image, and it is proved theoretically that the LSB matching will smooth the image histogram and histogram of difference image. Accordingly, the absolute differences between adjacent elements of image histogram are calculated as the histogram features, and co-occurrence matrix is utilized to extract features based on image correlation. A calibrated image is generated by embedding a message into the pending image. The features are extracted from both pending and calibrated images, and the ratios of corresponding features between pending and calibrated images are used as the final features. A support vector machine is utilized to train the classifier with the extracted features. Experimental results show that the proposed features outperform some previous ones and reveal the respective strong points of histogram and correlation features in the detection of never-compressed and JPEG-compressed images.

Paper Details

Date Published: 12 August 2013
PDF: 10 pages
J. Electron. Imag. 22(3) 033008 doi: 10.1117/1.JEI.22.3.033008
Published in: Journal of Electronic Imaging Volume 22, Issue 3
Show Author Affiliations
Zhihua Xia, Nanjing Univ. of Information Science & Technology (China)
Shufang Wang, Nanjing Univ. of Information Science & Technology (China)
Xingming Sun, Nanjing Univ. of Informatin Science & Technology (China)
Baowei Wang, Nanjing Univ. of Information Science & Technology (China)

© SPIE. Terms of Use
Back to Top