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The GFR (Gabor Filter Residual) features, built as histograms of quantizedresiduals obtained with 2D Gabor filters, can achieve competitive detectionperformance against adaptive JPEG steganography. In this paper, an improvedversion of the GFR is proposed. First, a novel histogram merging method isproposed according to the symmetries between different Gabor filters, thusmaking the features more compact and robust. Second, a new weighted histogrammethod is proposed by considering the position of the residual value in aquantization interval, making the features more sensitive to the slight changesin residual values. The experiments are given to demonstrate the effectivenessof our proposed methods. Finally, we design a CNN to duplicate the detectorwith the improved GFR features and the ensemble classifier, thus optimizing thedesign of the filters used to form residuals in JPEG-phase-aware features.

Further Study on GFR Features for JPEG Steganalysis