Detecting Pixel-Value Differencing Steganography Using Levenberg-Marquardt Neural Network

With the wide use of steganographic techniques, several security challenges emerge, e. g. criminals and network intruders can hide any information they want into legitimate multimedia data and exchange it over the Internet. This requires network designers and service providers to investigate new tools for detecting such misuse. In this paper, we explore a detection method based on neural network approach with Levenberg-Marquardt back propagation learning algorithm. This learning technique has been known to overcome the slow convergence of traditional back propagation and the instability problem of the steepest descent optimization method. We focus on digital images containing messages embedded by one of the recently proposed steganographic methods, known as pixel-value differencing. The idea is to analyze images before and after embedding to extract discriminating features and then build a neural network recognition model. The proposed approach is empirically evaluated and compared with four other machine-learning methods. The results show that more than 99% detection rate can be attained with very few false alarms.