Steganalysis of spread spectrum data hiding exploiting cover memory

In this paper we study steganalysis, the detection of hidden data. Specifically we focus on detecting data hidden in grayscale images with spread spectrum hiding. To accomplish this we use a statistical model of images and estimate the detectability of a few basic spread spectrum methods. To verify the results of these findings, we create a tool to discriminate between natural “cover” images and “stego” images (containing hidden data) taken from a diverse database. Existing steganalysis schemes that exploit the spatial memory found in natural images are particularly effective. Motivated by this, we include inter-pixel dependencies in our model of image pixel probabilities and use an appropriate statistical measure for the security of a steganography system subject to optimal hypothesis testing. Using this analysis as a guide, we design a tool for detecting hiding on various spread spectrum methods. Depending on the method and power of the hidden message, we correctly detect the presences of hidden data in about 95% of images.