Steganography Methodology

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JPEG IMAGE STEGANALYSIS USING MACHINE LEARNING Abstract—This project deals with detection of steganography content. Steganography is the additional method in cryptography which helps to hide the coded message inside pictures, audio or videos. To hide the message is important but to reveal such content is more important to avoid usage by criminals. This project applies an approach of supervised machine learning to detect the presence of steganographic content coded by programs like Steghide in the images. Keywords—Steganography, Stego-images, Cover-images, Steganalysis. I. INTRODUCTION Steganography is the process of hiding the secret information within an ordinary message. Steganography applies to any type of the medium. The stegaogram…show more content…
Closely related is who is using steganography. Unfortunately, satisfactory answer to this is hard to find. A standard claim in the literature is that terrorist organizations uses steganography to plan their operations. This claim seems to be founded on a report in USA Today, where it claimed that Osama Bin Laden was using internet in an 'e-jihad' half a year before he become famous in September 2001. The major problem lies in determining whether file is a steganogram or not. With the wide use and abundance of steganography tools on the internet, law enforcement authorities have concerns in the trafficking of illicit material through web page images, audio and other files. Methods of detecting hidden information and understanding the overall structure of this technology are crucial in uncovering these activities. II.…show more content…
Training Sets For training it is necessary to define suitable training sets. We used photos from ground truth image database[11]. In this group of photos a secret message will be inserted using the program steghide. The message is unique in every image due to a random generator of strings that will be used. Huffman’s coding data from JPEGSnoop will be transferred to training set-all four columns are to given line by line which will create a vector. Examples of clear and coded inputs in a training set are in Figure 3 and Figure 4. {0,82,2811,886,837,724,547,213,44,0,0,0,0,0,0,0,0,537,494,602,542,475,293,112,17,0,0,0,0,0,0,0,0,111597,38817,46384,30163,5825,14139,6943,2526,2580,658,206,0,0,32,947,0,41239,30606,31571,18650,7639,724,3479,842,352,150,54,0,7,11,27} Figure 3: Example of clear input in training set {0,240,2734,853,811,715,535,212,44,0,0,0,0,0,0,0,0,534,497,603,542,474,293,112,17,0,0,0,0,0,0,0,0,111447,39851,46280,30122,5796,14067,6953,2498,2569,621,179,0,015,681,0,41366,30474,31522,18612,7645,716,3524,847,357,158,54,0,7,11,28} Figure 4: Example of coded input in training set As number show there is a difference but here are the examples of two pictures without and with secret messages inside (figure 5 and figure 6). For the first view there is no

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