Fast additive noise steganalysis

This work reduces the computational requirements of the additive noise steganalysis presented by Harmsen and Pearlman. The additive noise model assumes that the stegoimage is created by adding a pseudo-noise to a coverimage. This addition predictably alters the joint histogram of the image. In color images it has been shown that this alteration can be detected using a three-dimensional Fast Fourier Transform (FFT) of the histogram. As the computation of this transform is typically very intensive, a method to reduce the required processing is desirable. By considering the histogram between pairs of channels in RGB images, three separate two-dimensional FFTs are used in place of the original three-dimensional FFT. This method is shown to offer computational savings of approximately two orders of magnitude while only slightly decreasing classification accuracy.