Damageless Information Hiding Technique using Neural Network

1. Damageless Information Hiding Technique using Neural Network Keio University Graduate School of Media and Governance Kensuke Naoe

2. Abstract • An information hiding technique without embedding any data to target content • Pattern recognition model • Neural network as classifier (extraction key) • Advantage and disadvantage

3. Outline • Background • Motivation • Current Problem • Proposed Method • Experiment results • Future Work • Coclusion

4. Background • Emergence of the Internet • Contents are widely distributed • Information hiding provides reliability • Digital watermarking for Digital Rights Management • Steganography for covert channel

5. Motivation and current problem • Use one information hiding algorithm with another to strengthen the security of the content • Digital watermarking • Steganography • FIngerprinting • There are many great information hiding algorithm but have difficulties to collaborate • possibility of obstructing previously embedded data • Applying another information hiding algorithm might result in recalculation of fingerprint for the content

6. Research Objective • To hide or to relate certain information without embedding any information to the target content • Ability to collaborate with another information hiding algorithm to strengthen the security

7. Proposed Method • Approach • Embed model to pattern recognition model • Neural network as classifier (extraction key) • Only proper extraction key will lead to proper hidden signal

8. Why use neural network? • Has abilities of • Tolerance to noise • Error correction and complementation • Additional learning characteristic • Multi-layered Perceptron Model • Backpropagation Learning (Supervised Learning)

9. Proposed Method (Embedding) 1.Frequency Transformation of content Coordinate of feature subblocks (extraction key) 2.Selection of feature subblock 3.Use feature values as input value for neural network Hidden signal as teacher signal 4. Generation of classifier (extraction key)

10. Proposed Method (Extraction) 1.Frequency Transformation of content Coordinate of feature subblocks (encryption key) 2.Selection of feature subblock 3.Use feature values as input value for neural network Hidden signal as output signal 4. Applying the classifier (encryption key)

11. What is neural network? • neuron（nervous cell） • It only has a function of receiving a signal and dispatching signal to connected neuron • When organically connected, it has ability to process a complicated task • A network built with these neurons are called neural network • Multi layered perceptron model • Often used for non-linear pattern classifier

12. Calculation of network • Input value of neuron • Sum product of network weight and output values from previous layer yj j xj wNj w1j wij y1 yN yi

13. Generating classifier (extraction key) 1.Frequency Transformation of content Coordinate of feature subblocks (encryption key) 2.Selection of feature subblock 3.Use feature values as input value for neural network Hidden signal as teacher signal 4. Generation of classifier (encryption key)

14. Patterns and signals to conceal

15. Backpropagation learning

16. Network 1

17. Network 2

18. Network 3

19. Network 4

20. Network 5

21. Further experiments • Can proposed method extract from high pass filtered image or jpeg image ？

22. Network 1

23. Network 2

24. Network 3

25. Network 4

26. Network 5

27. Network 1

28. Network 2

29. Network 3

30. Network 4

31. Network 5

32. Future work • Because it relies on the position of feature sub block, it is weak to geometric attacks • Rotation, expansion, shrinking • Key sharing has to rely on another security technology

33. Conclusion • Information hiding technique without embedding any data into target content by using neural network • Ability to collaborate with other information hiding algorithm

34. Thank you

35. Appendix

36. Tradeoffs for information hiding

37. Output layer Hidden layer Input layer Three layered perceptron model • Three layer model • Feed forward model • Input function • Sigmoid function • Backpropagation learning

38. x y Sigmoid function • Input function for multi-layered perceptron model • sigmoid = look like letter of S

39. ８ ８ Selection of feature values Has DC value and various values of AC (low, middle, high) Feature subblock

40. number of hidden neuron=10threshold=0.05

41. number of hidden neuron=10threshold=0.1

42. number of hidden neuron=20threshold=0.1