Improving Steganalysis by Fusing SVM Classifiers for JPEG Images
Peiqing Liu, Fenlin Liu, Chunfang Yang, Xiaofeng Song · 2015
As the present fusing strategies cannot utilize the correlation of different detection results for image steganography effectively, a steganalysis method is proposed based on fusing SVM classifiers. Firstly, different feature subsets are used for the training of SVM classifiers. Secondly, the detection results of multi-classifiers are utilized to train a fusing classifier, the fusing classifier can learn the correlation and diversity of detection results of sub-classifiers. From the experimental result, it can be seen that the proposed steganalysis method can achieve better detection performance for J-UNIWARD steganography compared with voting and Bayesian methods.