Further Study on GFR Features for JPEG Steganalysis
This work addresses steganalysis for security applications, but it is incremental as it builds on existing GFR methods.
The paper tackles the problem of detecting adaptive JPEG steganography by improving GFR features, resulting in more compact, robust, and sensitive features that achieve competitive detection performance.
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.