Generalized transfer component analysis for mismatched JPEG steganalysis
Most universal JPEG steganalysis approaches rely on the assumption that training and testing samples come from the same distribution. They fail when training set and testing set are mismatched. In this paper, we propose generalized transfer component analysis for mismatched JPEG steganalysis to derive new representations from original features for training and testing samples to correct the mismatches. We first apply domain alignment to transform source domain (training set) to an intermediate domain closer to target domain (testing set). Then a set of common transfer components are learnt across two domains by minimizing the distribution distance between them. In the space spanned by these transfer components, two domains manifest similar characteristics and preserve enough discrimination to different categories. Extensive experiments demonstrate our method performs well in mismatched JPEG steganalysis.