The implementation of steganography in text domain is one the crutial issue that can hide an essential message to avoid the intruder. It is caused every personal information mostly in medium of text, and the steganography itself is expectedly as the solution to protect the information that is able to hide the hidden message that is unrecognized by human or machine vision. This paper concerns about one of the categories in steganography on medium of text called text steganography that specifically focus on feature-based method. This paper reviews some of previous research effort in last decade to discover the performance of technique in the development the feature-based on text steganography method. Then, ths paper also concern to discover some related performance that influences the technique and several issues in the development the feature-based on text steganography method.

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.

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This paper focuses on one of the areas of information hiding which is image steganography. It proposes the StegSVM model as an embedding technique in steganography that has exploited human visual system through Shifted LSB that shows an expected performance. The performance of this technique evaluation is based on imperceptibility and robustness of the technique compared to the other previous models in image steganography doamin. Thus, the result shows that the proposed StegSVM model is promising. For further work, it is suggested that the other image domain through other intelligent methods should be investigated.

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.