imperceptible to the human eye. These messages, however, alter the underlying statistics of
an image. We previously built statistical models using first-and higher-order wavelet
statistics, and employed a non-linear support vector machines (SVM) to detect
steganographic messages. In this paper we extend these results to exploit color statistics,
and show how a one-class SVM greatly simplifies the training stage of the classifier.