Images steganalysis using GARCH model for feature selection

• Using features from the GARCH model (which is usually used in economics) for image steganalysis.
• Modeling the wavelet coefficients of Image signals which are highly heavy-tail.
• Employing the higher order statistics to capture any footprint in the image left by data hiding.
• Applying the maximum likelihood rule for detection.

This paper introduces a new blind steganalysis method. The required image features are extracted based on generalized autoregressive conditional heteroskedasticity (GARCH) model and higher-order statistics of the images. These features are exploited during the classification stage to detect a variety of steganographic methods in both spatial and transform domains. The GARCH features are extracted from non-approximate wavelet coefficients, where the heavy-tailed distribution of the coefficients makes the GARCH model applicable. Besides, the second order statistics are used to develop features very sensitive to minor changes in natural images. The experimental results demonstrate that the proposed feature-based steganalysis method outperforms state of the art methods while running on the same order of the features.