Steganalysis aware steganography: statistical indistinguishability despite high distortion

We consider the interplay between steganographer and the steganalyzer, and develop a steganalysis aware framework for steganography. The problem of determining a stego image is posed as a feasibility problem subject to constraint of data communication, imperceptibility, and statistical indistinguishability with respect to steganalyzer’s features. A stego image is then determined using set theoretic feasible point estimation methods. The proposed framework is applied effectively on a state of the art steganalysis method based on higher order statistics (HOS) steganalysis. We first show that the steganographer can significantly reduce the classification performance of the steganalyzer by employing a statistical constraint during embedding, although the image is highly distorted. Then we show that steganalyzer can develop a counter-strategy against steganographer’s action, gaining back some classification performance. This interchange represents an empirical iteration in this game between the steganographer and steganalyzer. Finally we consider mixture strategies to find the Nash equilibrium of the interplay.