Improved universal quantitative steganalysis in spatial domain using ELM ensemble

Quantitative steganalysis seeks to extract the additional information about the hidden message in the covert communications. Most of the quantitative steganalyzers available in the literature target a specific embedding algorithm and generally extract the payload information using structural paradigm. Modern steganalyzers use supervised machine learning to estimate the stego payload using sophisticated feature sets. In this paper, an Ensemble Framework based universal quantitative steganalyzer for digital images is proposed which employs optimised Extreme Learning Machines as the base regressors. The framework exploits inherent diversity of the base regressor and the use of random subspaces of the image features further reduces the prediction error. The proposed ensemble regressor exhibits improved payload predictions when evaluated vis-à-vis the individual base regressor and other state-of-the-art algorithms. The experimental results across different embedding algorithms, image datasets and variedly sized feature sets demonstrate the robustness and wide applicability of the proposed framework.