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