Multiclass detect of current steganographic methods for JPEG format based re-stegnography

The aim of this paper is to properly classify various stego images of JPEG to their own stegnographic methods (current steganographic methods, such as F5, OutGuess, Steghide, JPhide and Jsteg). Although some Multiclass Detection methods had been previously published by the authors, they all had various limitations and disadvantages. First, models of some detect methods are too complicated, and their process are too fussy. Second, the performance of some detect methods could decline when the embed rate minish. Based on re-stegnography, the detection of this paper's algorithm extracts 109-dimensions features and trains SVM(support vector machine) multiclassifiers to classify all kinds of stego images and cover images with very high precisions (approximately 100%). Not only the model is very simple, but the performance is all the same excellent when the embed rate minish.