STEGO-HUNTER
(Attacking LSB Based Image Stegnographic Technique)

       Steganography is the process of hiding secret information in a cover image. This process allows user to hide large amount of information with an image are in audio files. In this process, first we have to encrypt the secret data and then hide it in an innocent data. The stego medium is obtained by the addition of cover medium , hidden data and stego key. The cover medium is the file in which we hide our secret data (hidden data).The cover medium is typically an image file or audio files. The stego medium is also the same type of file in the cover medium. The stego image should not contain any easily detectable information by the human eye. The Steganographic tools are used to detect the hidden message in the stego medium.

STEGANOGRAPHIC METHODS:

       There are several methods for hide our secret message in any image files or audio files. The commonly using approaches are as follows:

       •  Least Significant Bit (LSB) Insertion method.

       •  Frequency Domain Techniques.

       •  Spread Spectrum Techniques.

       •  Cover Generation methods

       •  Statistical methods.

       •  Fractal Techniques.

       The stego image will vary according to the hidden messages. In pratical, the most widely using and simplest Steganographic method is LSB insertion method.

STEGANOGRAPHIC TOOLS:

       The Steganographic tools are used to detect the secret data in the stego medium. The commonly used tools are as follows:

1.StegoDos.        5. S-Tools.

2.MandelSteg.      6. Ezstego.

3.Hide and Seek.      7. Hide4PGP.

4.Jpeg-Jsteg.         8. Steganos.

STEGO-ATTACK:

       In this paper, we innovated a unique stego-only attack in LSB insertion for color images. This attack is applied when the stego-image is available and the attacker has no idea about the original cover image,stego key and encoding algorithm.It is almost the best feasible attack in real world. Our goal is to inspect a set of images for statistical artifacts due to message embedding in color images using LSB insertion method and to find out, which images out of them are likely to be stego. Our decision of deciding the image as stego or untampered using the threshold value. The selection of threshold value determines the robustness of our paper in terms of false detection in positive and negative sides. There is tremendous improvement in the performance which will be shown at last.

 
CLOSE COLOR PAIR ANALYSIS:

       We have used a Steg-analysis method for uncompressed high-density color image format using the close color pair signature. In a natural uncompressed image (i.e. 24bit BMP) each image is represented by three color channels (Red, Green and Blue), each of the channel is 8 bits wide. Most methods hide the information in an uncompressed natural image which is based on replacing the LSB color channels by message bits. Thus, on the average only half of the LSB's are changed but ,the embedding message will not hamper the statistics of the cover image and in turn no detectable signatures will be generated.

       In a natural uncompressed image, the ratio of number of unique colors to the total number of pixels is approximately 1:6.Hence after LSB embedding, which is equivalent of introducing noise, the randomness of LSB pattern will increase. This increase will reflected in increase in the number of close color pairs. We are considering two colors namely (R1,G1,B1) and (R2,G2,B2), If these two colors are close if and only if

If these two colors are unique if and only if

Next, we have to find the value of R which is the relative number of close color pairs with the unique colors where ,

       We have observed that for an umtampered image (the image which does not contain any hidden message),the value of R is greater in comparison with the which has secret message embedded in it. This happens because the embedded message acts as a random noise ,which increases the number of unique colors abruptly.

       As an example, we have taken five 24bit BMP images of different in color composition of birds,fruits,animals,building etc.The ratio of R for images is shown in below table. We done this experiment for 10% hiding alone. The result of R is tabulated in Tabulation 1.

       At an absolute threshold ,the tampered water body image as untampered (false detection) one and an untampered land image as tampered (false alarm) one. After completed our testing, we have observed a particular property to distinguish the tampered image and an untampered image. The peculiar property is , if any test image is already tampered with a

       message, embedding it further with additional bit streams will not modify the R value significantly. Alternately, if the test image is untampered one, the ratio R decreases significantly when it is further tampered by additional bit streams.

       If U' and P' are the number of unique colors and close color pairs respectively then, gives the relative number of close color pair in the artificially tampered image I'.

The change in the ratio R is measured in terms of m where, m is the percentage change in R defined as:

 
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