Steganalysis of content-adaptive JPEG steganography based on Gauss partial derivative filter bank

Yi Zhang; Fenlin Liu; Chunfang Yang; Xiangyang Luo; Xiaofeng Song; Jicang Lu

doi:10.1117/1.JEI.26.1.013011

8 February 2017 Steganalysis of content-adaptive JPEG steganography based on Gauss partial derivative filter bank

Yi Zhang, Fenlin Liu, Chunfang Yang, Xiangyang Luo, Xiaofeng Song, Jicang Lu

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 1, 013011 (February 2017). https://doi.org/10.1117/1.JEI.26.1.013011

Abstract

A steganalysis feature extraction method based on Gauss partial derivative filter bank is proposed in this paper to improve the detection performance for content-adaptive JPEG steganography. Considering that the embedding changes of content-adaptive steganographic schemes are performed in the texture and edge regions, the proposed method generates filtered images comprising rich texture and edge information using Gauss partial derivative filter bank, and histograms of absolute values of filtered subimages are extracted as steganalysis features. Gauss partial derivative filter bank can represent texture and edge information in multiple orientations with less computation load than conventional methods and prevent redundancy in different filtered images. These two properties are beneficial in the extraction of low-complexity sensitive features. The results of experiments conducted on three selected modern JPEG steganographic schemes—uniform embedding distortion, JPEG universal wavelet relative distortion, and side-informed UNIWARD—indicate that the proposed feature set is superior to the prior art feature sets—discrete cosine transform residual, phase aware rich model, and Gabor filter residual.

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Yi Zhang, Fenlin Liu, Chunfang Yang, Xiangyang Luo, Xiaofeng Song, and Jicang Lu "Steganalysis of content-adaptive JPEG steganography based on Gauss partial derivative filter bank," Journal of Electronic Imaging 26(1), 013011 (8 February 2017). https://doi.org/10.1117/1.JEI.26.1.013011

Received: 16 November 2016; Accepted: 12 January 2017; Published: 8 February 2017

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