Title :
GA-Based LSB-Matching Steganography to Hold Second-Order Statistics
Author :
Liu, Guangjie ; Zhang, Zhan ; Dai, Yuewei ; Wang, Zhiquan
Author_Institution :
Sch. of Autom., Nanjing Univ. of Sci. & Technol., Nanjing, China
Abstract :
How to design more secure steganographical algorithms all along is the hot topic. In this paper, a modified LSB-matching method is proposed to hold second-order statistics described by Markov Chain distance. To achieve it, the genetic algorithm is used to find the optimum tuning vector to match LSBs of image pixels and message bits. Experiments show the proposed algorithm has better security in K-L and M-C distance meanings, and the blind steanalysis tests also show that our new algorithm is more secure than LSB and LSB-matching methods.
Keywords :
Markov processes; blind source separation; genetic algorithms; image coding; image matching; statistical analysis; steganography; K-L distance meanings; LSB-matching steganography; M-C distance meanings; Markov chain distance; blind steanalysis tests; genetic algorithm; image pixels; message bits; optimum tuning vector; second-order statistics; Algorithm design and analysis; Automation; Genetic algorithms; Higher order statistics; Information security; Quantization; Statistical distributions; Steganography; Stochastic processes; Testing; LSB-matching; Markov chain; genetic algorithm; steganography;
Conference_Titel :
Multimedia Information Networking and Security, 2009. MINES '09. International Conference on
Conference_Location :
Hubei
Print_ISBN :
978-0-7695-3843-3
Electronic_ISBN :
978-1-4244-5068-8
DOI :
10.1109/MINES.2009.281
