Human beings have always wanted to communicate in secure ways. For many reasons such as transmitting a data on the network or sending a message to a friend, exchanging the data in secret is essential. There are many solutions to keep the data secure, among which, an important one is cryptography. Sensitive information has been protected using encryption from since many years ago. In cryptography, powerful mathematics is used to change plaintext into an unreadable coded text that is sent over a channel to the receiver. The other method of communication, called steganography, offers data protection in a different manner. Steganography is the science of data communication securely in a digital media, such as image, audio, and video files among them image files are most common, especially JPEG images, because these images are very popular and widely used. Along with the rapid growth and improvements of steganographic techniques, the attentions are turned into detection of these secret messages as well. This process is called Steganalysis. Usually, steganalysis methods are divided into two categories: specific steganalysis and blind steganalysis. The specific steganalysis can recover the secret message or even estimate the embedding ratio with the knowledge of the steganographic algorithm. However, implementation of this steganalysis method is very hard, because detection of steganography algorithm is difficult for steganalyzers or even the embedding algorithm may be unknown. But blind steganalysis can detect the secret message independent of the embedding algorithm and is commonly used in many applications. Blind steganalysis is done in two phases. First, the features that are changed with data embedding are extracted from images. Then a justify; LINE-HEIGHT: normal; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr" Hence, many of the current blind steganalysis methods are based on extracting features which are liable to change under data embedding. These methods have various performances in steganographic algorithms. Hence it is impossible to choose a specific feature with same performance on all steganographic algorithms. Moreover, blind steganalysis methods do not take advantage of the content diversity of images and the justify; LINE-HEIGHT: normal; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr" Keywords Blind Steganalysis, Data Clustering, Feature Extraction, Classification, Feature Selection
