Locating secret messages in images

ABSTRACT

Steganography involves hiding messages in innocuous media such as images, while steganalysis is the field of detecting these secret messages. The ultimate goal of steganalysis is two-fold: making a binary classification of a file as stego-bearing or innocent, and secondly, locating the hidden message with an aim to extracting, sterilizing or manipulating it. Almost all steganalysis approaches (known as attacks) focus on the first of these two issues. In this paper, we explore the difficult related problem: given that we know an image file contains steganography, locate which pixels contain the message. We treat the hidden message location problem as outlier detection using probability/energy measures of images motivated by the image restoration community. Pixels contributing the most to the energy calculations of an image are deemed outliers. Typically, of the top third of one percent of most energized pixels (outliers), we find that 87% are stego-bearing in color images and 61% in grayscale images. In all image types only 1% of all pixels are stego-bearing indicating our techniques provides a substantial lift over random guessing.

REFERENCES

• 1: http://www.jjtc.com/stegoarchive/stego/software.html
• 2: Johnson, N., Duric, Z., and Jajodia, S. Information Hiding: Steganography and Watermarking, 2001.
• 3: Starr, B., and Utley, G. CNN, July 23, 2002: http://www.cnn.com/2002/US/07/23/binladen.internet
• 4: http://www.cise.nsf.gov/accomp/index.cfm?div=iis
• 5: Kelley, J. USA Today, July 10, 2002: http://www.usatoday.com/news/world/2002/07/10/web-terror-cover.htm
• 6: Provos, N. Defending against Statistical Steganalysis. Proc. 10th USENIX Security Symposium, 2001.
• 7: Andreas Westfeld , Andreas Pfitzmann, Attacks on Steganographic Systems, Proceedings of the Third International Workshop on Information Hiding, p.61-76, September 29-October 01, 1999
• 8: Siwei Lyu , Hany Farid, Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines, Revised Papers from the 5th International Workshop on Information Hiding, p.340-354, October 07-09, 2002
• 9: Berg, G., Davidson, I., Duan, M., and Paul, G., Searching for Hidden Messages: Automatic Detection of Steganography. 15th Innovative App. of A.I., 2003, 51--56.
• 10: Fridrich, J., and Goljan, M. Practical Steganalysis of Digital Images - State of the Art. Proc. SPIE Photonics West, Vol. 4675, 2002, 1--13
• 11: Jiawei Han , Micheline Kamber, Data mining: concepts and techniques, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000
• 12: Charu C. Aggarwal , Philip S. Yu, Outlier detection for high dimensional data, Proceedings of the 2001 ACM SIGMOD international conference on Management of data, p.37-46, May 21-24, 2001, Santa Barbara, California, United States
• 13: Graham Williams , Rohan Baxter , Hongxing He , Simon Hawkins , Lifang Gu, A Comparative Study of RNN for Outlier Detection in Data Mining, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), p.709, December 09-12, 2002
• 14: Knorr, E. M., and Ng, R. T. A unified notion of outliers: Properties and computation, Proc. KDD, 1997, 219--222.
• 15: Shashi Shekhar , Chang-Tien Lu , Pusheng Zhang, Detecting graph-based spatial outliers: algorithms and applications (a summary of results), Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.371-376, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502567]
• 16: Anil K. Jain, Fundamentals of digital image processing, Prentice-Hall, Inc., Upper Saddle River, NJ, 1989
• 17: Qi Li , Jieping Ye , Chandra Kambhamettu, Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data, Proceedings of the Third IEEE International Conference on Data Mining, p.163, November 19-22, 2003
• 18: Barry A. Cipra, An introduction to the Ising model, American Mathematical Monthly, v.94 n.10, p.937-959, Dec., 1987 [doi>10.2307/2322600]
• 19: Neal, R. Probabilistic Inference Using Markov Chain Monte Carlo Method, TR CRG-TR-93-1, U. of Toronto.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation

General Terms:
Algorithms, Experimentation, Security

Keywords:
outlier detection, steganalysis, steganography