Universal image steganalysis based on GARCH model

This paper introduces a new universal steganalysis framework. The required image features are extracted based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and higher-order statistics of the images. The GARCH features are extracted from non-approximate wavelet coefficients. Besides, the second and third order statistics are exploited to develop features very sensitive to minor changes in natural images. The experimental results demonstrate that the proposed feature-based steganalysis framework outperforms state of the art methods while running on the same order of features.