JPEG Steganalysis Using the Relations Between DCT Coefficients

Abstract

Increasing attention to steganalysis and steganography due to the need for secure information transfer is one of the most important concerns of communication. Among the several image formats, JPEG is the most widely used compression method today. As a result, various stenographic systems based on disguising messages in jpeg format have been presented. Consequently, steganalysis of JPEG images is very essential. Recently, using neural networks and deep learning has greatly increased both in spatial and JPEG steganalysis. However, in the field of JPEG steganalysis, most of the existing networks still utilized hand-designed components as well. In the proposed JPEG steganalysis method we investigate the relations of the quantized Discrete Cosine Transform (DCT) coefficients and extract the binary vectors as the input of the neural network employing the relations of mid-frequency coefficients. The experimental results illustrate the acceptable detection rate of the simple presented approach.