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  1. 1. By -chandana kaza
  2. 2. <ul><li>Steganography </li></ul><ul><li>Steganalysis </li></ul><ul><li>Conventional methods </li></ul><ul><li>Machine learning based steganalysis </li></ul><ul><li>Experiments and results </li></ul>
  3. 3. <ul><li>Transmit secret messages. </li></ul><ul><li>To make transferred secret messages undetectable. </li></ul><ul><li>Embed messages in such a way so as not be detected. </li></ul>
  4. 5. <ul><li>Passive warden-examines and determines whether the message contains hidden message. </li></ul><ul><li>Active warden-alters the message, even though there is no trace of secret message. </li></ul>
  5. 8. <ul><li>Not suited </li></ul><ul><ul><li>Images with low number of colors </li></ul></ul><ul><ul><li>Images with unique semantic content </li></ul></ul><ul><li>Best suited </li></ul><ul><ul><li>Gray scale images </li></ul></ul><ul><ul><li>Uncompressed scans of photographs </li></ul></ul><ul><ul><li>Images captured by digital camera </li></ul></ul>
  6. 9. <ul><li>Simple and straight forward </li></ul><ul><li>Embed message into the least significant bit plane. </li></ul><ul><li>Difficult to be found by human eye. </li></ul>
  7. 11. <ul><li>Cover image </li></ul><ul><li>Stego image </li></ul>
  8. 12. <ul><li>To detect the existence of steganography </li></ul><ul><li>Estimate the message length </li></ul><ul><li>Extract hidden information </li></ul><ul><li>Achieved by exploiting differences between files. </li></ul>
  9. 13. <ul><li>2 types of LSB embedding </li></ul><ul><ul><li>Sequential </li></ul></ul><ul><ul><li>Non-sequential </li></ul></ul><ul><ul><li>Classifying of steganalysis techniques </li></ul></ul><ul><ul><li>Instance based </li></ul></ul><ul><ul><li>Non-instance based </li></ul></ul>
  10. 15. <ul><li>The  2 Method [Pfitzmann and Westfeld] </li></ul><ul><li>Splits images into segments </li></ul><ul><li>Calculate  2 co-efficients for every segments </li></ul><ul><li>Decide whether there is hidden message. </li></ul><ul><li>Suitable for sequentisl LSB </li></ul>
  11. 16. <ul><li>If embedded message bit and original bit are different then, flip bit. </li></ul><ul><li>Technique </li></ul><ul><ul><li>Let pixel value=j </li></ul></ul><ul><ul><li>If j=2i, after flip j=2i+1 </li></ul></ul><ul><ul><li>If j=2i+1, after flip j=2i </li></ul></ul>
  12. 17. <ul><li>Combines 2 pixel values 2i and 2i+1 together as a pair, and the two values differ in the lowest bit. </li></ul>
  13. 19. <ul><li>Where k is the total number of all possible pixels. </li></ul><ul><li>If p is close to 1 then image is embedded. </li></ul>
  14. 20. <ul><li>Suitable for any type of situation. </li></ul><ul><li>Exploits spatial correlation in images. </li></ul><ul><li>Based on analyzing how the number of R and S groups changes with the increased message length embedded in LSB plane. </li></ul>
  15. 21. <ul><li>Technique </li></ul><ul><ul><li>Consider a MXN image with pixel values from set p. </li></ul></ul><ul><ul><li>For gray scale p={0……255} </li></ul></ul><ul><ul><li>Divide the image into disjoint groups of n adjacent pixels. </li></ul></ul><ul><ul><li>Define a discrimination function f(xl . . . . . xn)ER that assigns a real number to each pixel group </li></ul></ul>
  16. 22. <ul><li>Purpose of the function is to quantify the smoothness or “regularity” of the group of pixels. </li></ul><ul><li>The noisier the group of pixels G=(x1….xn), the larger the value of the discrimination function. </li></ul>
  17. 23. <ul><li>Define an invertible operation F on p called flipping. </li></ul><ul><li>LSB Flipping F 1 :0<->1,2<->3,….,254<->255 </li></ul><ul><li>Shifted Lsb Flipping F -1 :-1<->0,1<->2,….,255<->256 </li></ul>
  18. 24. <ul><li>Depending on the above operation we define 3 pixel groups </li></ul><ul><li>Where F(G)=(F(x1),….f(xn)) </li></ul><ul><li>The flipping function can be captured by a mask M, which is a n-tuple with values -1,0,1. </li></ul>
  19. 25. <ul><li>Number of R groups for mask M:RM </li></ul><ul><li>Number of singular groups:SM </li></ul><ul><li>Statistical hypothesis </li></ul><ul><ul><li>For typical image </li></ul></ul><ul><ul><li>This theory is violated after randomizing the LSB plane. </li></ul></ul>
  20. 26. <ul><li>Randomization of the LSB plane forces </li></ul><ul><li>ie, the difference tend to become zero as the message length increases. </li></ul><ul><li>In the case of R -M and S -M the opposite happens. </li></ul>
  21. 27. <ul><li>P is the length of message. </li></ul><ul><li>The initial measurement of R and S groups is R M( p/2), S M (p/2), R- M (p/2), and </li></ul><ul><li>S- M (p/2) </li></ul>
  22. 28. <ul><li>The points RM(p/2), RM(1/2), RM(1-p/2) and SM(p/2), SM(1/2), SM(1-p/2) determine 2 parabolas. </li></ul><ul><li>Now calculate the root of the quadratic equation </li></ul>
  23. 31. <ul><li>Learning denotes changes in the system that enable the system to do the same task more effectively next time. </li></ul><ul><li>Ex: classify an object as an instance </li></ul>
  24. 32. <ul><li>The conventional methods just used some hypothesis observed heuristically. </li></ul><ul><li>If they are differences between the real model and fixed model, they will fail. </li></ul><ul><li>ML is used to reduce the errors brought by fixed models. </li></ul>
  25. 33. <ul><li>For hidden information detection simple classifiers are used. </li></ul><ul><li>Using machine learning the quality of classifiers will be improved and successively more stable performance can be acquired. </li></ul>
  26. 34. <ul><li>Hidden information process is treated as classification process. </li></ul><ul><li>I/p-images </li></ul><ul><li>O/p-class labels </li></ul><ul><li>The data set is built based on the values in  2 and RS methods. </li></ul>
  27. 37. <ul><li>Training set : A portion of data set used to fit(train) a model for prediction or classification of values that are known in the training set, but unknown in (future)data. </li></ul><ul><li>Test set : A set of data used only to assess the performance [generalization] of a fully-specified classifier. </li></ul><ul><li>Feature extraction : Transforming the input data into the set of features to reduce redundant information is called features extraction. </li></ul>
  28. 38. <ul><li>24 bit color images are collected </li></ul><ul><li>Embed different length of messages into images </li></ul><ul><li>Extract features using different methods for sequential and non-sequential cases. </li></ul><ul><li>Perform preprocessing </li></ul><ul><li>Every image will result an instance represented by a set of features in the data set. </li></ul>
  29. 39. <ul><li>Build the experiment platform on WEKA </li></ul><ul><li>Test results on different machine learning methods. </li></ul><ul><ul><li>Naïve Bayes </li></ul></ul><ul><ul><li>Bayes networks </li></ul></ul><ul><ul><li>Decision trees </li></ul></ul><ul><ul><li>KNN </li></ul></ul><ul><ul><li>SVM </li></ul></ul><ul><ul><li>Neural networks </li></ul></ul>
  30. 40. <ul><li>Apply ML-based classifier on POV3 algorithm. </li></ul><ul><li>The LSB bit-plane is treated as sequential pixel samples and is split into 100 segments. </li></ul><ul><li>For every segment, the  2 probability for all the pixels from the first segment to current one is calculated. </li></ul><ul><li>We get 300 coefficients, simple  2 then make a decision according to a threshold. </li></ul><ul><li>But our method uses these as features and construct classifiers. </li></ul>
  31. 43. <ul><li>Precision α 1/Image complexity </li></ul><ul><li>Precision α embed rate </li></ul>
  32. 44. <ul><ul><li>Because most of the pictures are in high complexity level 2-4, so ML-based methods generally perform better than simple  2 . </li></ul></ul><ul><ul><li>Conclude that applying machine learning to  2 can effectively improve the accuracy </li></ul></ul><ul><ul><li>Classifier wrapped conventional steganalysis maybe a good solution to detect sequential LSB steganography. </li></ul></ul>
  33. 45. <ul><li>We use RS approach in this case </li></ul><ul><li>The differences between R±M(p/2), and S±M(p/2), increase when message length p increases. </li></ul><ul><li>The features are calculated using the difference </li></ul>
  34. 46. <ul><li>Direct difference is not used in order to reduce the bias between different images. </li></ul><ul><li>Test this feature based methods with ML techniques. </li></ul><ul><li>Main focus is on the change of embed rate, the difference between different intrinsic complexities. </li></ul>
  35. 47. Precision (RMS) Embed 0.1 Embed 0.2 Embed 0.5 Embed 1.0 Embed All Mixed Naive Bayes 50.90%(0.59) 53.80%(0.57) 54.90%(0.45) 94.56%(0.31) 80.48%(0.37) Bayes Net 89.21%(0.27) 95.85%(0.17) 99.35%(0.07) 99.45%(0.07) 95.44%(0.18) kNN 92.16%(0.28) 97.55%(0.16) 99.45%(0.07) 99.70%(0.05) 96.38%(0.19) J48 94.11%(0.27) 98.05%(0.14) 99.40%(0.08) 99.65%(0.06) 97.56%(0.14) SMO 59.39%(0.64) 75.32%(0.50) 93.21%(0.26) 96.70%(0.18) 80.90%(0.44) BP 53.10%(0.50) 54.10%(0.50) 52.70%(0.50) 56.80%(0.50) 80.00%(0.40) Threshold RS 95.30% 98.75% 99.70% 87.11% 97.38%
  36. 48. <ul><ul><li>From table, we can see that J48 performs best in mixed embed rate case, and can get nearly 98% accuracy at all embed levels. </li></ul></ul><ul><ul><li>We use only two features, this result is comparable to  2 case in sequential embedding and is better than threshold based RS can do. </li></ul></ul>
  37. 49. <ul><li>1. http://www.cs.waikato.ac.nz/ml/weka/ . </li></ul><ul><li>2. http://www.outguess.org/ . </li></ul><ul><li>3. S. Antani, R. Kasturi, and R. Jain. A survey on the use of pattern recognition methods for abstraction,indexing and retrieval of images and video. Pattern Recognition, 35(4):945{965, 2002. </li></ul><ul><li>4. G. Berg, I. Davidson, M.-Y. Duan, and G. Paul. Searching for hidden messages: Automatic detection of steganography. In IAAI, pages 51-56, 2003. </li></ul><ul><li>5. M. Morkel. Steganography And Steganalysis, ICSA Research Group. University of Pretoria, South Africa. January 2005. </li></ul><ul><li>6. Y.-M. Di, H. Liu, A. Ramineni, and A. Sen. Detecting hidden information in images: A comparative study. In 2nd Workshop on Privacy Preserving Data Mining (PPDM), 2003. </li></ul><ul><li>7. S. Dumitrescu, X. Wu, and Z. Wang. Detection of lsb steganography via sample pair analysis. In Information Hiding 5th International Workshop IH 2002 Revised Papers, Lecture Notes in Computer Science vol. 2578, pages 355{372, 2003. </li></ul><ul><li>8. J. J. Fridrich. Feature-based steganalysis for jpeg images and its implications for future design of steganographic schemes. In Information Hiding 6 th International Workshop IH 2004 Revised Selected Papers, Lecture Notes in Computer Science vol. 3200, pages 67{81, 2004. </li></ul>
  38. 50. Thank you