Boosting Steganalysis with Explicit Feature Maps

Explicit non-linear transformations of existing steganalysis features are shown to boost their ability to detect steganography in combination with existing simple classifiers, such as the FLD-ensemble. The non-linear transformations are learned from a small number of cover features using Nystrom approximation on pilot vectors obtained with kernelized PCA. The best performance is achieved with the exponential form of the Hellinger kernel, which improves the detection accuracy by up to 2-3% for spatial-domain contentadaptive steganography. Since the non-linear map depends only on the cover source and its learning has a low computational complexity, the proposed approach is a practical and low cost method for boosting the accuracy of existing detectors built as binary classifiers. The map can also be used to significantly reduce the feature dimensionality (by up to factor of ten) without performance loss with respect to the non-transformed features.