Steganalysis of Content-Adaptive JPEG Steganography Based on CNN and 2D Gabor Filters

Aiming at the problem of choosing high-pass filters in image processing layer of deep CNN (Convolution Neural Network) for steganalysis, this paper studies the effect of different filter banks on detection performance for content-adaptive JPEG steganography, and proposes a steganalysis method based on multi-scale 2D Gabor filtering and ensemble of multiple deep CNNs. Firstly, the effect of high-pass filters of image processing layer on detection performance is studied and the detection errors corresponding to the different combinations of high-pass filters are shown, then the relevant experimental results are analyzed. Lastly, two typical content-adaptive JPEG steganography algorithms such as UED and J-UNIWARD are taken as examples, the proposed steganalysis method is compared with other methods to verify the effectiveness of the proposed method.