1. A Neural-Network Approach for Visual Cryptography 虞台文 大同大學資工所

2. Content Overview The Q’tron NN Model The Q’tron NN Approach for – Visual Cryptography – Visual Authorization – Semipublic Encryption General Access Scheme Conclusion

3. A Neural-Network Approach for Visual Cryptography Overview 大同大學資工所

4. What is Visual Cryptography and Authorization? Visual Cryptography (VC) – Encrypts secrete into a set of images (shares). – Decrypts secrete using eyes. Visual Authorization (VA) – An application of visual cryptography. – Assign different access rights to users. – Authorizing using eyes.

5. What is Semipublic Encryption? Visual Cryptography (VC) – Encrypts secrete into a set of images (shares). – Decrypts secrete using eyes. Semipublic Encryption (SE) – An application of visual cryptography. – Hide only secret parts in documents – Right information is available if and only if a right key is provided

6. The Basic Concept of VC Target Image (The Secret) Share 2 Share 1 Access Scheme Access Scheme The (2, 2) access scheme.

7. The Shares Produced by NN Target Image (The Secret) Share 2 Share 1 Neural Network Neural Network We get shares after the NN settles down.

8. Decrypting Using Eyes Share 2 Share 1

9. Example: (2, 2) Target image Share image2 Share image1 Plane shares are used

10. Traditional Approach Naor and Shamir (2,2) PixelProbability Shares #1 #2 Superposition of the two shares White Pixels Black Pixels The Code Book

11. The VA Scheme key share user shares (resource 2) user shares (resource 1) stacking … … VIP IP P … VIP IP P V ery I mportant P erson. …

12. The SE Scheme 智慧型系統實驗室資料庫 使用者 Key 江素貞 AB 陳美靜 CD 張循鋰 XY 李作中 UV 智慧型系統實驗室資料庫 使用者 Key 江素貞 AB 陳美靜 CD 張循鋰 XY 李作中 UV

13. public share (database in lab) ABCDXYUV stacking user shares keys 素貞 The SE Scheme 循鋰美靜作中

14. A Neural-Network Approach for Visual Cryptography The Q’tron NN Model 大同大學資工所

15. The Q’tron  i (a i )  i (a i )... 012 qi1qi1 aiQiaiQi Active value Q i  {0, 1, …, q i  1} IiRIiR External Stimulus Internal Stimulus NiNi Noise Quantum Neuron

16. The Q’tron  i (a i )  i (a i )... 012 qi1qi1 aiQiaiQi Active value Q i  {0, 1, …, q i  1} IiRIiR External Stimulus Internal Stimulus NiNi Noise Free-Mode Q’tron

17. The Q’tron  i (a i )  i (a i )... 012 qi1qi1 aiQiaiQi Active value Q i  {0, 1, …, q i  1} IiRIiR External Stimulus Internal Stimulus NiNi Noise Clamp-Mode Q’tron

18. Input Stimulus Internal Stimulus ExternalStimulus Noise Free Term  i (a i )  i (a i )... Noise

19. Level Transition Running Asynchronously  i (a i )  i (a i )...

20. Energy Function Interaction Among Q’trons Interaction with External Stimuli Constant Monotonically Nonincreasing

21. The Q’tron NN

22. Interface/Hidden Q’trons clamp-mode free-mode free mode  Hidden Q’trons Interface Q’trons

23. Question-Answering Feed a question by clamping some interface Q’trons. clamp-mode free-mode free mode  Hidden Q’trons Interface Q’trons

24. Question-Answering Read answer when all interface Q’trons settle down. clamp-mode free-mode free mode  Hidden Q’trons Interface Q’trons

25. A Neural-Network Approach for Visual Cryptography The Q’tron NNs for Visual Cryptography Visual Authorization Semipublic Encryption 大同大學資工所

26. Energy Function for VC Visual Cryptography Image Halftoning Image Stacking +

27. Image Halftoning Graytone Image Halftoning 0 255 Halftone Image 0 (Transparent) 1 Graytone image  halftone image can be formulated as to minimize the energy function of a Q’tron NN.

28. Image Halftoning Graytone Image Halftoning 0 255 Halftone Image 0 (Transparent) 1 Graytone image  halftone image can be formulated as to minimize the energy function of a Q’tron NN. In ideal case, each pair of corresponding small areas has the `same’ average graylevel.

29. The Q’tron NN for Image Halftoning Plane- G (Graytone image) Plane- H (Halftone image)

30. Image Halftoning Halftoning Clamp-mode Free-mode Plane- G (Graytone image) Plane- H (Halftone image) Question Answer

31. Image Restoration Plane- G (Graytone image) Plane- H (Halftone image) Restoration Clamp-mode Free-mode Question Answer

32. Stacking Rule ++++ The satisfaction of stacking rule can also be formulated as to minimize the energy function of a Q’tron NN.

33. Stacking Rule ++++ The satisfaction of stacking rule can also be formulated as to minimize the energy function of a Q’tron NN. The energy function for the stacking rule. See the paper for the detail.

34. The Total Energy + Share 1 Target Share 1 Share 2 TargetShare 2 Total Energy Image Halftoning Stacking Rule

35. The Q’tron NN for VC/VA Plane-GS1 Plane-HS1 Share 1 Plane-HS2 Plane-GS2 Share 2 Plane-GT Plane-HT Target

36. Application  Visual Cryptography Plane-GS1 Plane-HS1 Share 1 Plane-HS2 Plane-GS2 Share 2 Plane-GT Plane-HT Target Clamp-Mode Free-Mode

37. Application  Visual Authorization Plane-GS1 Plane-HS1 User Share Authority Plane-HS2 Plane-GS2 Plane-GT Plane-HT Key Share User Share VIPIPP

38. Application  Visual Authorization Plane-GS1 Plane-HS1 User Share Authority Clamp-Mode Free-Mode Plane-HS2 Plane-GS2 Clamp-Mode Free-Mode Plane-GT Plane-HT Clamp-Mode Free-Mode Key Share User Share VIPIPP Producing key Share & the first user share.

39. Application  Visual Authorization Plane-GS1 Plane-HS1 User Share Authority Clamp-Mode Plane-HS2 Plane-GS2 Clamp-Mode Free-Mode Plane-GT Plane-HT Clamp-Mode Some are clamped and some are free. Key Share User Share VIPIPP Producing other user shares.

40. Application  Visual Authorization Plane-GS1 Plane-HS1 User Share Authority Clamp-Mode Plane-HS2 Plane-GS2 Clamp-Mode Free-Mode Plane-GT Plane-HT Clamp-Mode Some are clamped and some are free. Key Share User Share VIPIPP Producing other user shares.

41. Application  Visual Authorization Plane-GS1 Plane-HS1 User Share Authority Clamp-Mode Plane-HS2 Plane-GS2 Clamp-Mode Free-Mode Plane-GT Plane-HT Clamp-Mode Some are clamped and some are free. Key Share User Share VIPIPP

42. Key Share User Share VIP IP P

43. A Neural-Network Approach for Visual Cryptography General Access Scheme 大同大學資工所

44. Full Access Scheme  3 Shares 朝辭白帝彩雲間 朝 辭 白 帝彩雲 間 Shares

45. Full Access Scheme  3 Shares 朝辭白帝彩雲間 朝 辭 白 帝彩雲 間 Shares Theoretically, unrealizable. We did it in practical sense. Theoretically, unrealizable. We did it in practical sense.

46. Full Access Scheme  3 Shares S1S2S3 S1+S2S1+S3S2+S3S1+S2+S3

47. Access Scheme with Forbidden Subset(s) Anyone knows what is it?

48. Access Scheme with Forbidden Subset(s) 人之初性本善 人 之 初 性本 X 善 Theoretically, realizable. Shares

49. Access Scheme with Forbidden Subset(s) S1S2S3 S1+S2S1+S3S2+S3S1+S2+S3

50. A Neural-Network Approach for Visual Cryptography Conclusion 大同大學資工所

51. Conclusion Different from traditional approaches: – No codebook needed. – Operating on gray images directly. Complex access scheme capable. http://www.suchen.idv.tw/

52. 謝謝