JPEG Steganalysis Based on Multi-Projection Ensemble Discriminant Clustering

Abstract

In this paper, we propose a novel algorithm called multi-projection ensemble discriminant clustering (MPEDC) for JPEG steganalysis. The scheme makes use of the optimal projection of linear discriminant analysis (LDA) algorithm to get more projection vectors by using the micro-rotation method. These vectors are similar to the optimal vector. MPEDC combines unsupervised K-means algorithm to make a comprehensive decision classification adaptively. The power of the proposed method is demonstrated on three steganographic methods with three feature extraction methods. Experimental results show that the accuracy can be improved using iterative discriminant classification.