Applications of Explicit Non-Linear Feature Maps in Steganalysis

Abstract

Currently, the most popular detectors of content-adaptive image steganography are built using machine learning with images represented with rich features. Such high-dimensional descriptors, however, prevent utilization of more complex and potentially more accurate machine learning paradigms, such as kernelized support vector machines, due to infeasibly expensive training. In this paper, we demonstrate that explicit non-linear feature maps coupled with simple classifiers improve the accuracy of current steganalysis detectors built as binary classifiers as well as quantitative detectors in the form of payload regressors. The non-linear map is obtained by approximating a symmetric positive semi-definite kernel on selected pairs of cover features. Exponential forms of kernels derived from symmetrized Ali-Silvey distances improve the detection accuracy of binary detectors and lower the error of quantitative detectors across all tested steganographic schemes on grayscale and color images. The learned non-linear map only weakly depends on the cover source and its learning has a low computational complexity. The technique can also be used for unsupervised feature dimensionality reduction. For payload regressors, the dimensionality can be significantly reduced while simultaneously decreasing the estimation error.