Embedded Feature Selection Approach Using Penalized Logistic Regression for Universal Steganalysis

Abstract

For universal steganalysis, the Spatial Rich Model (SRM) provides a robust feature set in the form of 106 feature submodels (34671-dimensional) to attack highly secure steganography methods. Nonetheless, the curse of dimensionality emerges as a drawback, prompting researchers to explore feature selection methods to achieve optimal performance for steganalyzers. In previous work, a wrapper feature selection method using global and local best particle swarm optimization (GLBPSO) has been used to select 28 submodels of SRM out of 106 submodels. The 28 submodels contain a total of 9152 features and an ensemble classifier has been used for the classification results. In this study, an embedded feature selection and classification approach using logistic regression (LR) with an elastic net penalty has been proposed and applied to the 9152 features. The proposed approach efficiently selects an informative subset of 9152 features while training the LR classifier. This not only reduces the number of features by a significant 75% but also boosts the classification performance when contrasted with the ensemble classifier. The proposed approach achieves an accuracy of 87.41%, surpassing the current state-of-the-art steganalysis methods based on deep learning by a substantial margin of 3-7%. It also outperforms a recent machine learning-based steganalysis method by 13%.