An Efficient JPEG Steganalysis Model Based on Deep Learning

Convolutional neural networks (CNN) have gained an overwhelming advantage in many domains of pattern recognition. CNN's excellent data learning ability and automatic feature extraction ability are urgently needed for image steganalysis research. However, the application of CNN in image steganalysis is still in its infancy, especially in the field of JPEG steganalysis. This paper presents an efficient CNN-based JPEG steganographic analysis model which is called JPEGCNN. According to the pixel neighborhood model, JPEGCNN calculates the pixel residual as a network input with a 3 x 3 kernel function. In this way, JPEGCNN not only solves the problem that direct analysis of DCT coefficients is greatly affected by image content, but also solves the problem that larger kernel functions such as 5 x 5 do not effectively capture neighborhood correlation changes. Compared with the JPEG steganographic analysis model HCNN proposed by the predecessors, JPEGCNN is a lightweight structure. The JPEGCNN training parameters are about 60,000, and the number of parameters is much lower than the number of parameters of the HCNN. At the same time of structural simplification, the simulation results show that JPEGCNN still maintains accuracy close to HCNN.