A Neural Network Approach for Visual Cryptography Tai-Wen Yue and Suchen Chiang IEEE 2000.

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Presentation transcript:

A Neural Network Approach for Visual Cryptography Tai-Wen Yue and Suchen Chiang IEEE 2000

What is Visual Cryptography? Target image Share image2 Share image1

The Procedure Plane-G Plane-S1 (Share 1 ) Plane-H Plane-S2 (Share 2 ) The original taget image Halftone Plain Shadow Image

Image Halftoning Error Aggregated Scan-Line Algorithm e0E0 e0e1E1 e0e1e2E2

The Q ’ tron(Quantum) NN for (2, 2) Plane-G Plane-S1 (Share 1 ) Plane-H Plane-S2 (Share 2 )

Q ’ tron---Q-State Neuron External stimulus Active value Internal stimulus

Q ’ tron---Q-State Neuron Q ’ tron ’ s Input Internal Stimulus External Stimulus Noise

Q ’ tron---Q-State Neuron State Updating Rule:

The Q ’ tron(Quantum) NN Plane-G Plane-S1 (Share 1 ) Plane-H Plane-S2 (Share 2 ) halftoning restoration +

Image Halftoning Energy Function: Sum of graylevel in a 3×3 area for graytone image Sum of halftone in a 3×3 area for halftone image  Minimizing the error square corresponds to halftoning -

Image Restoration Energy Function: - (i,j) i j r=3

s1s1 s2s h E2E s1s1 s2s2 h E2E2 FeasibleInfeasible LowHigh Cost Function

Stacking Rule Satisfaction Energy Function: _ Minimizing this term tends to satisfy the stacking rules

Share Image Assignment For simplicity, shares are plain images S1 S2 Mean Gray level K 1 K2K2 Result

Build Plain Shadow Images Energy Function: [g low,g high ] : K 1 =K 2 ≦ g low <g high ≦ min(2K,255) 例如 : [135,235] ; K1=K2=

Total Energy Halftoning Restoration Share Images Cost Function

Conclusions Share images size = target image size. Code book is free.

Future Works Design language to specify an access scheme. Extend to color images