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Information Technology Journal
Year: 2011  |  Volume: 10  |  Issue: 8  |  Page No.: 1601 - 1607

Detection of LSB Matching Steganography using Neighborhood Node Degree Characteristics

Bin Xia, Xingming Sun, Lingyun Xiang, Haijun Luo and Hengfu Yang    

Abstract: This study presented a method to detect Least Significant Bit (LSB) matching steganography in gray images based on the neighborhood node degree characteristic. Natural images have a strong correlation between adjacent pixels and it’s disturbed by LSB matching. Accordingly the effects of LSB matching steganography on neighborhood node degree were examined at first. Then features were extracted from neighborhood node degree histogram. A new calibration algorithm based on neighborhood node degree was proposed to get more effective features. Support Vector Machine (SVM) was used as classifier. Experimental results demonstrated that the proposed method was efficient to detect the LSB matching steganography and had superior results compared with other recently proposed algorithms on compressed images and low embedding rate uncompressed images.

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