Steganalysis of halftone images

We present a novel steganalysis technique for halftone images without knowledge of the original cover image. We first convert halftone images into grayscale-like images by low-pass filtering. The low-pass-filtered image is then decomposed using quadrature mirror filters, and a set of subband coefficients are generated at different scales and orientations. Next, a set of statistical features are computed from the subband coefficients and their predicated errors. Using Fisher linear discriminant analysis, a statistical classifier is designed to detect marked images. Experimental results demonstrate the effectiveness and accuracy of the proposed technique.