Steganalysis for GIF images based on colors-gradient co-occurrence matrix

A steganalysis algorithm based on colors-gradient co-occurrence matrix (CGCM) is proposed in this paper. CGCM is constructed with colors matrix and gradient matrix of the GIF image, and 27-dimensional statistical features of CGCM, which are sensitive to the color-correlation between adjacent pixels and the breaking of image texture, are extracted. Support vector machine (SVM) technique takes the 27-dimensional statistical features to detect hidden message in GIF images. Experimental results indicate that the proposed algorithm is more effective than Zhao's algorithm for several existing GIF steganographic algorithms and steganography tools, especially for multibit assignment (MBA) steganography and EzStego. Furthermore, the time efficiency of the proposed algorithm is much higher than Zhao's algorithm. (C) 2012 Elsevier B.V. All rights reserved.