Steganography detection using localized generalization error model

Steganography detection is a technique to tell whether there are secret messages hidden in images. The performance of a steganalysis system is mainly determined by the method of feature extraction and the architecture selection of the classifier. Selecting a proper classifier with proper parameters will improve the detection accuracy and generalization capability of the system. We propose a Radial Basis Function Neural Network (RBFNN) optimized by the Localized Generalization Error Model (L-GEM) for steganograhpy detection. In the proposed method, the discrete cosine transform (DCT) features and the Markov features are used as inputs of neural networks for detection. To enhance the generalization capability of the RBFNN and the performance of detecting steganography in future images, the architecture of the RBFNN is selected by minimizing the L-GEM. The experimental results show that the proposed method provides a better performance on testing images in comparison with the existing method in attacking Steghide, OutGuess and F5.