Performance analysis on text steganalysis method using a computational intelligence approach

In this paper, a critical view of the utilization of computational intelligence approach from the text steganalysis perspective is presented. This paper proposes a formalization of genetic algorithm method in order to detect hidden message on an analyzed text. Five metric parameters such as running time, fitness value, average mean probability, variance probability, and standard deviation probability were used to measure the detection performance between statistical methods and genetic algorithm methods. Experiments conducted using both methods showed that genetic algorithm method performs much better than statistical method, especially in detecting short analyzed texts. Thus, the findings showed that the genetic algorithm method on analyzed stego text is very promising. For future work, several significant factors such as dataset environment, searching process and types of fitness values through other intelligent methods of computational intelligence should be investigated.