Steganalysis for JPEG images based on manifold learning

Most current steganalysis schemes are based on the Cachin´s statistical undetectability. However, the lack of the statistical model for the natural images limits the performance of the current steganalyzers. In this paper, we propose a novel steganalyzer for JPEG images based on manifold learning, which overcomes the statistical steganalyzer´s deficiencies. The feature extraction in this steganalyzer is completed by the nonlinear dimensionality reduction method (ISOMAP), which will greatly reduce the dimensionality of the feature space without influencing the performance of our steganalyzer. Experimental results show the effectiveness of this method and also demonstrate the promise of the proposed scheme used both as a specific and a blind steganalyzer.