Universal Steganalysis of Data Hiding in Grayscale Images

摘要

This paper proposed an universal steganalysis program based on quantification attack which can detect several kinds of data hiding algorithms for grayscale images. In practice, most techniques produce stego images that are perceptually identical to the cover images but exhibit statistical irregularities that distinguish them from cover images. Attacking the suspicious images using the quantization method, we can obtain statistically different from embedded-and-quantization attacked images and from quantization attacked-but-not-embedded sources. We have developed a technique based on one-class SVM for discriminating between cover-images and stego-images. Simulation results show our approach is able to distinguish between cover and stego images with reasonable accuracy.