Research on Simulated Annealing clustering algorithm in the steganalysis of image based on the One-Class Support Vector Machine
Peng Luo, Yang Su · 2010
In this paper, a novel and effective steganalysis based on One-Class Support Vector Machine(OC-SVM) with Simulated Annealing clustering algorithm is proposed to blindly (i.e., without knowledge of the steganographic schemes) determine the existence of hidden messages in an image. The performance of sample clustering is concerned in the OC-SVM with multi-sphere. In previous work, the K-means is mainly used to create such multi-sphere by clustering. But the traditional K-means depends on initial clustering centers and ends local minimum value. So, to solve the problem caused by K-means, the Simulated Annealing is employed into the proposed scheme, which can create more reasonable multi-sphere by finding global optimum solutions in the clustering process. Simulation results with the chosen feature set and well-known steganographic techniques indicate that our approach is able to afford reasonable accuracy to distinguish between covers and stego images.