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Presentation of Dissertation we had on Image Steganography in MCA Study.

Presenters: Vikas Kantiya and Hushen Savani

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- 1. Guided by, Dr. Jyoti Pareek Department of Computer Science Rollwala Computer Center Gujarat University Ahmedabad Hushen Savani (24) Vikas Kantiya (10) MCA-V
- 2. What is Steganography? • • • • • • • • • • • • • • • Steganography Framework Categories Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Steganography is the art and science of writing hidden messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message.
- 3. Steganography Framework • • • • • • • • • • • • • • • Steganography Framework Categories Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics
- 4. Categories of Steganography • • • • • • • • • • • • • • • Steganography Framework Categories Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics
- 5. What is Image Steganography? • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Image Steganography is the technique of hiding the data within the image in such a way that prevents the unintended user from the detection of the hidden messages or data. For example, Cover Image Data / Message Stego Image
- 6. Applications of Image Steganography • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Secure Private Files and Documents. Hide Passwords and Encryption Keys. Transport Highly Private Documents between International Governments. Transmit message/data without revealing the existence of available message.
- 7. Image Domain • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics What Images are made up of?:Images are made up of lots of little dots called pixels. Each pixel is represented as 3 bytes – one for Red, one for Green and one for Blue. Red Blue 11111000 11001001 00000011 248 201 3 Each byte is interpreted as an integer number, which is how much of that color is number used to make the final color of the pixel. 248 + 201 + 3 = Orange Color
- 8. Image Domain • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics The difference between two colors that differ by one bit in either one red, green or blue value is impossible detect for a human eye. eye So we can change the least significant (last) bit in a byte, we either add or subtract one or more values from the value it represents. This means we can overwrite the last bit in a byte without affecting the colors it appears to be.
- 9. Image Domain • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics A common approach of hiding data within an image file is Least Significant Bit (LSB) Substitution. Substitution In this method, we can take the binary representation of the hidden data and overwrite the LSB of each byte within the cover image.
- 10. Least Significant Bit Substitution • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Suppose we have the following binary representation for the Cover Image. 10010101 00001101 10010110 00001111 Suppose we want to "hide" the following 4 bits of data: 1011, we get the following, 10010101 00001101 10010110 00001111 Where the each data bits are accommodated in the least significant bits of each byte of the image.
- 11. Least Significant Bit Substitution • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Least Significant Bit Substitution results in a very minor distortion of the image which is very much negligible for the human eyes. Cover Image Stego Image
- 12. Substitution Levels • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics 4-bits 5-bits 6-bits 7-bits
- 13. Pixel Indicator • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics This method uses the least two significant bits of one of the channels to indicate existence of data in the other two channels. channels
- 14. Pixel Indicator • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Example, R G B Initial Pixel Bytes: 10101101 11011010 11100101 Data to be Embedded: 1101 Channel R: 10101111 Channel G: 11011001 Channel B: 11100111 Indicating Channel Pixel Indicator Bits Channels in which Data is Embedded
- 15. Stego Color Cycle • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics The SCC technique uses the RGB images to hide the data in different channels. It keeps cycling the hidden data between the Red, Green and Blue channels, utilizing one channel at a cycle time.
- 16. Triple-A • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Triple-A technique uses the same principle of LSB, where the secret is hidden in the least significant bits of the pixels, with more randomization in selection of the number of bits used and the color channels that are used. • Two Seeds: • To determine the used channels • To determine the number of bits used This randomization is expected to increase the security of the system.
- 17. Max-bit • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics This method measures the intensity of the pixel and then hides data by random pixel selection with a goal to hide maximum data in each pixel. This method is divided into three parts: Encryption Image Intensity Calculation Steganography.
- 18. Max-bit • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Original Image
- 19. Max-bit • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Grayscale Image * All Black colored pixels are considered as Intense pixels. Intense Pixels*
- 20. Statistics • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Triple-A SCC Max-bit Bits/pixel 3.428 3 6.281 Capacity Ratio 3.43/24 => 14.28% 3/24 => 12.5% 6.28/24 => 26.1%
- 21. Optimum Pixel Adjustment Procedure • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Optimal Pixel adjustment Procedure (OPAP) reduces the distortion caused by the LSB substitution method. In OPAP method the pixel value is adjusted after the hiding of the secret data. This done to improve the quality of the stego image without disturbing the data hidden.
- 22. Optimum Pixel Adjustment Procedure • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics First a few least significant bits are substituted with the data to be hidden. Then in the pixel, the bits before the hidden bits are adjusted suitably if necessary to give less error. Let n LSBs be substituted in each pixel. Let d= decimal value of the pixel after the substitution. d1 = decimal value of last n bits of the pixel. d2 = decimal value of n bits hidden in that pixel.
- 23. Optimum Pixel Adjustment Procedure • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics If(d1~d2)<=(2^n)/2 then no adjustment is made in that pixel. Else If(d1<d2) d = d – 2^n . If(d1>d2) d = d + 2^n . Where, d is converted to binary and written back to pixel
- 24. Optimum Pixel Adjustment Procedure • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Retrieval Process of Data: Data The retrieval follows the extraction of the least significant bits(LSB) as hiding is done using simple LSB substitution.
- 25. Inverted Pattern • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics This inverted pattern (IP) LSB substitution approach uses the idea of processing secret messages prior to embedding. In this method each section of secret images is determined to be inverted or not inverted before it is embedded. In addition, the bits which are used to record the transformation are treated as secret keys or extra data to be reembedded.
- 26. Inverted Pattern • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics The embedded string is S, the replaced string is R, and the embedded bit string to divided to P parts. Let us consider n-bit LSB substitution to be made. Then S and R are of n-bits length. For P part in i = 1 to P If MSE(Si,Ri) ≤ MSE(S’i,Ri) Choose Si for embedding Mark key(i) as logic ‘0’ If MSE(Si,Ri) ≥ MSE( S‘i,Ri) Choose S‘ i for embedding Mark key(i) as logic ‘1’ End For Where, MSE = Mean Squared Error S is the data to be hidden S‘ is the data to be hidden in inverted form.
- 27. Inverted Pattern • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Retrieval Process of Data: Data The stego-image and the key file are required at the retrieval side. First corresponding numbers of LSB bits are retrieved from the stego-image. If the key is ‘0’, then the retrieved bits are kept as such. Else if the key is ‘1’, then the bits are inverted. The bits retrieved in this manner from every pixel of the stego-image gives the data hidden.
- 28. IP Method Using Relative Entropy • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Relative entropy measures the information discrepancy between two different sources with an optimal threshold obtained by minimizing relative entropy. In this method, instead of finding the mean square error for inverted pattern approach, the relative entropy is calculated to decide whether S or S‘ suites the pixel.
- 29. IP Method Using Relative Entropy • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Divide the cover image into P blocks of same size, the embedding string is S, and the replaced string is R. For P part in i =1 to P If rel.entropy(Si,Ri) ≤ rel.entropy (S‘i,Ri) Choose Si for embedding Mark key(i) as logic ‘0’ If rel.entropy (Si,Ri) ≥ rel.entropy (S‘i,Ri) Choose S‘i for embedding Mark key(i) as logic ‘1’ End For Where, S is the data to be hidden S‘ is the data to be hidden in inverted form.
- 30. Decision Factors • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Peak Signal to Noise Ratio (PSNR): The PSNR is calculated using the equation, where Imax is the intensity value of each pixel which is equal to 255 for 8 bit gray scale images. Mean Square Error (MSE): The MSE is calculated using the equation, where M and N denote the total number of pixels in the horizontal and the vertical dimensions of the image Xi, j represents the pixels in the original image and Yi, j, represents the pixels of the stego image.
- 31. Pixel Value Differencing • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Pixel Value Differencing (PVD) is able to provide a high quality stego image in spite of the high capacity of the concealed information. That is, the number of insertion bits is dependent on whether the pixel is an edge area or smooth area. In edge area the difference between the adjacent pixels is more, whereas in smooth area it is less.
- 32. Pixel Value Differencing • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Smooth Area Edge Area • While human perception is less sensitive to subtle changes in edge areas of a pixel, it is more sensitive to changes in the smooth areas.
- 33. Pixel Value Differencing • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics This method hides the data in the target pixel by finding the characteristics of four pixels surrounding it, indicated in the table below:
- 34. Pixel Value Differencing • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Select the maximum and the minimum values(g) among the three pixel values that have already finished the embedding process. Consider upper pixel (g1), left pixel (g2) and the upper left pixel (g3) in a given target pixel g(x,y) Calculate d using following: d= [max (g1, g2, g3) – min (g1, g2, g3)] Using d , we get an idea as to whether the target pixel is included in an edge area or in smooth area.
- 35. Pixel Value Differencing • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics Calculation of n: the number of the insertion bits in a target pixel Px,y is calculated, using the following formula:
- 36. Statistics • • • • • • • • • • • • • • • Steganography Categories Framework Image Steganography Applications Image Domain -- Methods -LSB PI SCC Triple-A Max-bit • Statistics OPAP Inverted Pattern • MSE based • Entropy based PVD • Statistics OPAP IP PVD Size of secret data 25 kb 24.5 kb 22 kb Image Size 242 kb 242 kb 242kb MSE 0.1503 0.2635 12.4715 PSNR 56.71 51.86 37.87 Time (s) 7.65 7.96 8.6

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