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Steganalysis of least significant bit matching based on image histogram and correlation

[+] Author Affiliations
Zhihua Xia

Nanjing University of Information Science & Technology, Jiangsu Engineering Center of Network Monitoring, No. 219, Ningliu Road, Nanjing City, Jiangsu Province, 210044 China

Nanjing University of Information Science & Technology, School of Computer & Software, No. 219, Ningliu Road, Nanjing City, Jiangsu Province, 210044 China

Shufang Wang

Nanjing University of Information Science & Technology, Jiangsu Engineering Center of Network Monitoring, No. 219, Ningliu Road, Nanjing City, Jiangsu Province, 210044 China

Nanjing University of Information Science & Technology, School of Computer & Software, No. 219, Ningliu Road, Nanjing City, Jiangsu Province, 210044 China

Xingming Sun

Nanjing University of Information Science & Technology, Jiangsu Engineering Center of Network Monitoring, No. 219, Ningliu Road, Nanjing City, Jiangsu Province, 210044 China

Nanjing University of Information Science & Technology, School of Computer & Software, No. 219, Ningliu Road, Nanjing City, Jiangsu Province, 210044 China

Baowei Wang

Nanjing University of Information Science & Technology, Jiangsu Engineering Center of Network Monitoring, No. 219, Ningliu Road, Nanjing City, Jiangsu Province, 210044 China

Nanjing University of Information Science & Technology, School of Computer & Software, No. 219, Ningliu Road, Nanjing City, Jiangsu Province, 210044 China

J. Electron. Imaging. 22(3), 033008 (Aug 12, 2013). doi:10.1117/1.JEI.22.3.033008
History: Received January 7, 2013; Revised April 28, 2013; Accepted July 9, 2013
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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.

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© 2013 SPIE and IS&T

Citation

Zhihua Xia ; Shufang Wang ; Xingming Sun and Baowei Wang
"Steganalysis of least significant bit matching based on image histogram and correlation", J. Electron. Imaging. 22(3), 033008 (Aug 12, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.3.033008


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