Multi-class Blind Steganalysis Based on Image Run-Length Analysis
- Cite this paper as:
- Dong J., Wang W., Tan T. (2009) Multi-class Blind Steganalysis Based on Image Run-Length Analysis. In: Ho A.T.S., Shi Y.Q., Kim H.J., Barni M. (eds) Digital Watermarking. IWDW 2009. Lecture Notes in Computer Science, vol 5703. Springer, Berlin, Heidelberg
In this paper, we investigate our previously developed run-length based features for multi-class blind image steganalysis. We construct a Support Vector Machine classifier for multi-class recognition for both spatial and frequency domain based steganographic algorithms. We also study hierarchical and non-hierarchical multi-class schemes and compare their performance for steganalysis. Experimental results demonstrate that our approach is able to classify different stego images according to their embedding techniques based on appropriate supervised learning. It is also shown that the hierarchical scheme performs better in our experiments.
Keywordsblind steganalysis multi-class image steganalysis run-length analysis
Unable to display preview. Download preview PDF.