A Blind Steganalytic Scheme Based on DCT and Spatial Domain for JPEG Images

In this paper, we propose a novel blind steganalytic scheme able to detect JPEG stego images embedded with several known steganographic programs. By estimating the original image of the given image, thirteen types of statistics are collected in the DCT domain and the decompressed spatial domain. Then we calculate the histogram characteristic function (HCF) and the center of mass (COM) for each statistic, and obtain a 77-dimensional feature vector for each image. Support vector machine (SVM) is utilized to construct the blind classifiers. Experimental results demonstrate that the proposed scheme provides better performance in terms of detection accuracy and false positive compare with several known blind approaches. In addition, we construct a multi-classifier capable of recognizing the steganography used for embedding in a stego image. At last, a universal steganalyzer is built, and the experimental results show that it is possible to recognize a new or yet not to be developed embedding algorithm by the steganalyzer.