Detection of perturbed quantization (PQ) steganography based on empirical matrix

Document Type: ORIGINAL RESEARCH PAPER

Authors

Abstract

Perturbed Quantization (PQ) steganography scheme is almost undetectable with the current steganalysis methods. We present a new steganalysis method for detection of this data hiding algorithm. We show that the PQ method distorts the dependencies of DCT coefficient values; especially changes much lower than significant bit planes. For steganalysis of PQ, we propose features extraction from the empirical matrix. The proposed features can be exploited within an empirical matrix of DCT coefficients which some most significant bit planes were deleted. We obtain four empirical matrices and fuse resulted features from these matrices which have been employed for steganalysis. This technique can detect PQ embedding on stego images with 77 percent detection accuracy on mixed embedding rates between 0.05 _ 0.4 bits per non-zero DCT AC coefficients (BPNZC). Comparing the results, we also show that the detection rates are effectively comparable with respect to current steganalysis techniques for PQ steganography.

Keywords


[1] Stegoarchive.com, 2009. Available at: http://stegoarchive.com.

[2] Neil F. Johnson. Steganography tools, 2009.mAvailable at: http://www.jjtc.com/Security/stegtools.htm.

[3] N. Provos. Defending Against Statistical Steganalysis. In Proceedings of the 10th USENIX Security Symposium, pages 323-335, Washington DC, USA, 2001.

[4] P. Sallee. Model-based Methods for Steganography and Steganalysis. International Journal of Image and Graphics (IJIG), 5(1):167-190, 2005.

[5] A. Westfeld. F5 - A Steganographic Algorithm: High Capacity Despite Better Steganalysis. In Proceedings of the 4th Internation Workshop on Information Hiding (IH'01), volume 2137 of Lecture Notes in Computer Science (LNCS), pages 289-302, Pittsburgh, PA, USA, 2001. Springer Verlag.

[6] J. Fridrich, M. Goljan, and D. Soukal. Perturbed Quantization Steganography with Wet Paper Codes. In Proceedings of the ACM Workshop on Multimedia and Security, pages 4-15, Magdeburg, Germany, 2004.

[7] R. Chandramouli,M. Kharrazi, and N. D. Memon. Image Steganography and Steganalysis: Concepts and Practice. In Proceedings of the 2nd International Workshop on Digital Watermarking (IWDW'03), volume 2939 of Lecture Notes in Computer Science (LNCS), pages 209-211, Seoul, Korea, 2003. Springer Verlag.

[8] H. Farid and S. Lyu. Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines. In Proceedings of the 5th International Workshop on Information Hiding, volume 2578 of Lecture Noted in Computer Science (LNCS), pages 340{354, Noordwijkerhout, Netherlands, 2002. Springer Verlag.

[9] Y. Q. Shi, G. Xuan, D. Zou, J. Gao, C. Yang, Z. Zhang, P. Chai, W. Chen, and C. Chen. Steganalysis Based on Moments of Characteristic Functions Using Wavelet Decomposition, Prediction-Error Image, and Neural Network. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME05), Amsterdam, Netherlands, 2005.

[10] J. Fridrich. Feature-Based Steganalysis for JPEG Images and Its Implications. In Proceedings of the 6th International Workshop on Information

Hiding (IH'04), volume 3200 of Lecture Notes in Computer Science (LNCS), pages 67-81, Toronto, Canada, 2005. Springer Verlag.

[11] M. Kharrazi, H. T. Sencar, and N. Memon. Performance Study of Common Image Steganography and Steganalysis Technique. Journal of Electronic Imaging, 15(4):041104-1-041104-16, 2006.

[12] G. Gkhan, E. D. Ahmet, and I. Avcibas. Steganalytic Features for JPEG Compression-Based Perturbed Quantization. IEEE Signal Processing Letters, 14(3):205-208, 2007.

[13] K. Sullivan, U. Madhow, S. Chandrasekaran, and B. S. Manjunath. Steganalysis for Markov Cover Data with Applications to Images. IEEE Transactions on Information Forensics and Security, 1(2):275-287, 2006.

[14] T. Pevny and J. Fridrich. Merging Markov and DCT Features for Multi-Class JPEG Steganalysis. In Proceedings of the SPIE, Security, Steganography, and Watermarking of Multimedia Contents IX, volume 6505, pages 650503.1-650503.13, San Jose, CA, USA, 2007.

[15] Y. Q. Shi, C. Chen, and W. Chen. A Markov Process Based Approach to Effective Attacking JPEG Steganography. In Proceedings of the 8th International Workshop on Information Hiding (IH'06), volume 4437 of Lecture Notes in Computer Science (LNCS), pages 249-264, Alexandria, VA, USA, 2006. Springer Verlag.

[16] M. Abolghasemi, H. Aghainia, and K. Faez. Steganalysis of Perturbed Quantization (PQ) Steganography Based on Markov Chain Model. In Proceedings of the 17th Iranian Conference on Electrical Engineering (ICEE'09), pages 620-625, Tehran, Iran, 2009.

[17] M. Abolghasemi, H. Aghainia, K. Faez, and M.A Mehrabi. Steganalysis of LSB Matching Based on Co-occurrence Matrix and Removing Most Significant Bit Planes. In Proceedings of the 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pages 1527-1530, Harbin, China, 2008.

[18] R. M. Haralick. Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernatics SMC-3, SMC-3(6):610-621, 1973.

[19] Free Stock Photos, 2009. Available at: www.freephotos.com.