Parameter-estimation and algorithm-selection based United-Judgment for image steganalysis
- 103 Downloads
In order to synthetically utilize multiple steganalytic algorithms, and further improve the detection accuracy and enhance detection reliability, United-Judgment methods are researched and analyzed in this paper. According to the performance of each algorithm, United-Judgment methods for both blind and specific steganalysis are proposed based on parameter-estimation and algorithm-selection. Experiments are carried out for the former with seven typical blind detections and the latter one with five typical spatial domain steganalytic methods. Experimental results show that the proposed methods can synthetically utilize the existing multiple algorithms effectively, and achieve more reliable detection.
KeywordsSteganalysis United-Judgment Parameter-estimation Algorithm-selection
The authors are grateful to the technical committee of the International Conference on Multimedia Information Networking and Security 2009 for recommending this paper to the International Journal. They are also grateful to Dr. Shiguo Lian for his insightful and invaluable suggestions and comments.
This work is supported by the National Natural Science Foundation of China (Grant No. 60970141 and 60902102), the Found of Innovation Scientists and Technicians Troop Construction Projects of Henan Province (Grand No. 094200510008) and the Science and Technology Program of Zhengzhou City (Grant No. 083SGYG21125).
- 1.Böhme R (2005) Assessment of steganalytic methods using multiple regression models. In: Proc. of 7th International Workshop on Information Hiding, Barcelona, Spain, Springer Lecture Notes in Computer Science, vol 3727, pp 278–295Google Scholar
- 2.Böhme R, Ker AD (2006) A two-factor error model for quantitative steganalysis. In: Proc. of the SPIE: Security, Steganography and Watermarking of Multimedia Contents VIII, San José, California, USA, vol 6072, pp 59–74Google Scholar
- 4.Fridrich J (2005) Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes. In: Proc. of 6th International Workshop on Information hiding, Toronto, Canada, Springer Lecture Notes in Computer Science, vol 3200, pp 67–81Google Scholar
- 5.Fridrich J, Goljan M (2004) On estimation of secret message length in LSB steganography in spatial domain. In: Proc. of the SPIE: Security, Steganography and Watermarking of Multimedia Contents VI, San Jose, California, USA, vol 5306, pp 23–34Google Scholar
- 7.Harmsen JJ, Pearlman WA (2003) Steganalysis of additive noise modelable information Hiding. In: Proc. of the SPIE: Security, Steganography and Watermarking of Multimedia Contents V, Santa Clara, California, USA, vol 5020, pp 131–142Google Scholar
- 8.Kharrazi M, Sencar HT, Memon N (2006) Improving steganalysis by fusion techniques: a case study with image steganography. IEEE Transactions on Data Hiding and Multimedia Security I, Springer Lecture Notes in Computer Science, vol 4300, pp 123–137Google Scholar
- 10.Lu PZ, Luo XY, Tang QY, Shen L (2004) An improved sample pairs method for detection of LSB embedding. In: Proc. of 6th International Workshop on Information Hiding Workshop, Toronto, Canada, Springer Lecture Notes in Computer Science, vol 3200, pp 116–127Google Scholar
- 11.Lu JC, Liu FL, Luo XY, Yang CF (2009) United-Judgment methods based on parameter-estimation for image steganalysis. In: Proc. of International Conference on Multimedia Information Networking and Security, Wuhan, Hubei, China, vol 1, pp 500–504Google Scholar
- 12.Luo XY, Liu FL, Chen JM, Zhang YN (2008) Image universal steganalysis based on wavelet packet transform. In: Proceedings of 10th International Workshop on Multimedia Signal Processing, pp 780–784Google Scholar
- 13.Luo XY, Wang DS, Wu W, Liu FL (2009) Blind detection for image steganography: a system framework and implementation. Int J Innov Comput I 5(2):433–442Google Scholar
- 15.Shi YQ, Xuan GR, Yang CY, Gao JJ, Zhang ZP, Chai PQ, Zou DK, Chen CH, Chen W (2005) Effective Steganalysis Based on Statistical Moments of Wavelet Characteristic Function. In: Proc. of IEEE International Conference on Information Technology: Coding and Computing, Las Vegas, Nevada, USA, vol 1, pp 768–773Google Scholar
- 16.Xuan GR, Shi YQ, Huang C, Fu DD, Zhu XM, Chai PQ, Gao JJ (2006) Steganalysis using high-dimensional features derived from co-occurrence matrix and classwise non-principal components analysis. In: Proc. of IEEE International Workshop on Digital Watermarking, Jeju Island, Korea, Springer Lecture Notes in Computer Science, vol 4283, pp 49–60Google Scholar
- 17.Yu XY, Wang AM (2009) Steganalysis based on regression model and Bayesian network. In: Proc. of International Conference on Multimedia Information Networking and Security, Wuhan, Hubei, China, vol 1, pp 41–44Google Scholar