Improving Image steganalysis performance using a graph-based feature selection method

Amir Nouri, Alimohammad Nazari


Steganalysis is the skill of discovering the use of steganography algorithms within an image with low or no information regarding the steganography algorithm or/and its parameters. The high-dimensionality of image data with small number of samples has presented a difficult challenge for the steganalysis task. Several methods have been presented to improve the steganalysis performance by feature selection. Feature selection, also known as variable selection, is one of the fundamental problems in the fields of machine learning, pattern recognition and statistics. The aim of feature selection is to reduce the dimensionality of image data in order to enhance the accuracy of Steganalysis task. In this paper, we have proposed a new graph-based blind steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on community detection. The experimental results show that the proposed approach is easy to be employed for steganalysis purposes. Moreover, performance of proposed method is better than several recent and well-known feature selection-based Image steganalysis methods.


Image steganalysis; Feature selection; Graph clustering; Feature clustering

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