Detect Information-Hiding Type and Length in JPEG Images by Using Neuro-fuzzy Inference Systems

In this paper, we present a scheme of steganalysis of JPEG images with the use of polynomial fitting and computational intelligence techniques. Based on the Generalized Gaussian Distribution (GGD) model in the quantized DCT coefficients, the errors between the logarithmic domain of the histogram of the DCT coefficients and the polynomial fitting are extracted as features to detect the adulterated JPEG images and the untouched ones. Computational intelligence techniques such as Support Vector Machines (SVM), neuro-fuzzy inference system, etc. are utilized. Results show that, the designed method is successful in detecting the information-hiding types and the information-hiding length in the multi-class JPEG images.