Optimal Gabor Filters for Steganalysis of Content-Adaptive JPEG Steganography

• Xiaofeng Song,
• Fenlin Liu,
• Liju Chen,
• Chunfang Yang,
• Xiangyang Luo,

Vol. 11, No. 1, January 29, 2017
10.3837/tiis.2017.01.029, Download Paper (Free):

• Content-adaptive steganography
• jpeg
• Steganalysis
• 2D Gabor filter
• optimal selection

Abstract

The existing steganalysis method based on 2D Gabor filters can achieve a competitive detection performance for content-adaptive JPEG steganography. However, the feature dimensionality is still high and the time-consuming of feature extraction is relatively large because the optimal selection is not performed for 2D Gabor filters. To solve this problem, a new steganalysis method is proposed for content-adaptive JPEG steganography by selecting the optimal 2D Gabor filters. For the proposed method, the 2D Gabor filters with different parameter settings are generated first. Then, the feature is extracted by each 2D Gabor filter and the corresponding detection accuracy is used as the measure for filter selection. Next, some 2D Gabor filters are selected by a greedy strategy and the steganalysis feature is extracted by the selected filters. Last, the ensemble classifier is used to assemble the proposed steganalysis feature as well as the final steganalyzer. The experimental results show that the steganalysis feature extracted by the selected optimal 2D Gabor filters also can achieve a competitive detection performance while the feature dimensionality is reduced greatly.

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