Steganalysis classifier training via minimizing sensitivity for different imaging sources

Owing to the ever proliferation of digital cameras and image editing software, a large variety of JPEG quantization tables are used to compress JPEG images. As a result, learning-based steganalysis methods using a pre-selected quantization table for training images degrade significantly when the quantization table of testing images is different from the one used for training. Recognizing that it would be undesirable and not practical to train a steganalysis classifier with all possible quantization tables, we propose an approach that the differences in features extracted from images with different quantization tables are formulated as perturbations of those features. Then we define a stochastic sensitivity by the expected square of classifier output changes with respect to these feature perturbations to compute the robustness of classifiers with respect to perturbations. A Radial Basis Function Neural Network based steganalysis classifier trained by minimizing the sensitivity is proposed. Experimental results show that the proposed method outperforms learning methods such as Support Vector Machine and Radial Basis Function Neural Network without considering feature perturbations.