Segmentation Based Steganalysis of Spatial Images Using Local Linear Transform
Most of the traditional image steganalysis techniques are conducted on the entire image and do not take advantage of the content diversity. However, the steganalysis features are affected by image content, and the impact is more serious than embedding. This makes steganalysis to be a classification problem with bigger within-class scatter distances and smaller between-class scatter distances. In this paper, a steganalysis algorithm aimed at spatial steganographic methods which can reduce the differences of image statistical characteristics caused by image content is proposed. The given images are segmented to sub-images according to the texture complexity. Steganalysis features based on local linear transform are separately extracted from each sort of sub-images with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. Experimental results performed on several diverse image databases and circumstances demonstrate that the proposed method exhibits excellent performances.
KeywordsSteganalysis Image segmentation Texture analysis Local linear transform
This work was supported by the National Natural Science Foundation of China under grant No. 61272490 and No. 61602511. The authors would like to thank the reviewers for their insightful comments and helpful suggestions.
- 4.Zhang, J., Cox, I.J., Doerr, G.: Steganalysis for LSB matching in images with high-frequency noise. In: IEEE Workshop Multimedia Signal Processing, pp. 385–388 (2007)Google Scholar
- 5.Cai, K., Li, X., Zeng, T.: Reliable histogram features for detecting LSB matching. In: IEEE International Conference on Image Processing, , Hong Kong, pp. 1761–1764 (2010)Google Scholar
- 10.Farid, H.: Detecting hidden messages using higher-order statistical models. In: IEEE International Conference on Image Processing, New York, pp. 905–908 (2002)Google Scholar
- 11.Goljan, M., Fridrich, J., Holotyak, T.: New blind steganalysis and its implications. In: SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia, pp. 1–13 (2006)Google Scholar
- 12.Xuan, G., et al.: Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions. In: 7th International Workshop on Information Hiding, Barcelona, Spain, pp. 262–277 (2005)Google Scholar
- 14.Kawaguchi, E., Eason, R.O.: Principle and applications of BPCS-Steganography. In: SPIE Multimedia Systems and Applications, Boston, pp. 464–472 (1998)Google Scholar
- 18.Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: 12th International Workshop on Information Hiding, Calgary, AB, Canada, pp. 161–177 (2010)Google Scholar
- 19.Fridrich, J., Kodovský, J., Goljan, M., Holub, V.: Steganalysis of content-adaptive steganography in spatial domain. In: 13th International Workshop on Information Hiding, Prague, Czech Republic, pp. 102–117 (2011)Google Scholar
- 21.Holub, V., Fridrich, J., Denemark, T.: Random projections of residuals as an alternative to co-occurrences in steganalysis. In: SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics XV, vol. 8665, , San Francisco, CA, pp. 3–7 (2013)Google Scholar
- 22.Cancelli, G., Doërr, G., Barni, M., Cox, I.J.: A comparative study of ±1 steganalyzers. In: IEEE International Workshop Multimedia Signal Processing, Cairns, Australia, pp. 791–796 (2008)Google Scholar
- 29.Bows-2 (2007). http://bows2.gipsa-lab.inpg.fr/BOWS2OrigEp3.tgz
- 30.Schaefer, G., Stich, M.: UCID - An Uncompressed Colour Image Database. School of Computing and Mathematics, Nottingham Trent University, U.K. (2003)Google Scholar