Optimized feature extraction for learning-based image steganalysis

The purpose of image steganalysis is to detect the presence of hidden messages in cover photographic images. Supervised learning is an effective and universal approach to cope with the twin difficulties of unknown image statistics and unknown steganographic codes. A crucial part of the learning process is the selection of low-dimensional informative features. We investigate this problem from three angles and propose a three-level optimization of the classifier. First, we select a subband image representation that provides better discrimination ability than a conventional wavelet transform. Second, we analyze two types of features-empirical moments of probability density functions (PDFs) and empirical moments of characteristic functions of the PDFs-and compare their merits. Third, we address the problem of feature dimensionality reduction, which strongly impacts classification accuracy. Experiments show that our method outperforms previous steganalysis methods. For instance, when the probability of false alarm is fixed at 1%, the stegoimage detection probability of our algorithm exceeds that of its closest competitor by at least 15% and up to 50%.