Active steganalysis for histogram-shifting based reversible data hiding

This paper presents an innovative active steganalysis algorithm for reversible data hiding schemes based on histogram shifting. These schemes use histogram shifting to embed secret data in cover-images. However, some histogram patterns originating during the embedding procedure may be recognized readily by a steganalyst. The proposed algorithm analyzes the characteristics of histogram changing during the data embedding procedure, and then models these features into reference templates by using a 1 × 4 sliding window. A support vector machine is trained as the classifier for discriminating between cover-images and stego-images by adopting the template matching techniques. The hidden messages located at the histogram peak of the cover-image were further estimated by measuring the feature of adjacent histogram differences. Experimental results indicate that the proposed active steganalysis algorithm can effectively detect stego-images at low bit rates and estimate the hidden messages locations.