A Neural-Network Approach for Visual Cryptography

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A Neural-Network Approach for Visual Cryptography

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Content

• Overview
• The Qtron NN Model
• The Qtron NN Approach for
• Visual Cryptography
• Visual Authorization
• Semipublic Encryption
• General Access Scheme
• Conclusion

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A Neural-Network Approach for Visual Cryptography

• Overview

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What isVisual 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.

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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

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The Basic Concept of VC
The (2, 2) access scheme.
Share 1
Target Image (The Secret)
Share 2
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The Shares Produced by NN
We get shares after the NN settles down.
Neural Network
Share 1
Target Image (The Secret)
Share 2
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Decrypting Using Eyes
Share 1
Share 2
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Example (2, 2)
Plane shares are used
Share image1
Share image2
Target image
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Traditional Approach

• Naor and Shamir (2,2)

The Code Book
White Pixels
Black Pixels
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The VA Scheme
Very Important Person.
user shares (resource 1)

user shares (resource 2)
key share

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The SE Scheme
??????????? ??? Key ??? AB ??? CD ???
XY ??? UV
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The SE Scheme
public share (database in lab)
user shares
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AB
CD
XY
UV
keys
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A Neural-Network Approach for Visual Cryptography

• The Qtron
• NN Model

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The Qtron
Quantum Neuron
?i (ai )
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The Qtron
Free-Mode Qtron
External Stimulus
Ii?R
?i (ai )
Ni
Noise
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The Qtron
Clamp-Mode Qtron
External Stimulus
Ii?R
. . .
?i (ai )
qi?1
0
1
2
Ni
Noise
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Input Stimulus
?i (ai )
Noise
Noise
Internal Stimulus
Noise Free Term
External Stimulus
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Level Transition
?i (ai )
Running Asynchronously
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Energy Function
Monotonically Nonincreasing
Interaction Among Qtrons
Constant
Interaction with External Stimuli
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The Qtron NN
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Interface/Hidden Qtrons
Interface Qtrons
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Question-Answering
Feed a question by clamping some interface
Qtrons.
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Question-Answering
Read answer when all interface Qtrons settle
down.
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A Neural-Network Approach for Visual Cryptography

• The Qtron NNs for
• Visual Cryptography
• Visual Authorization
• Semipublic Encryption

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Energy Function for VC
Visual Cryptography
Image Halftoning
Image Stacking

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Image Halftoning
Graytone image ? halftone image can be formulated
as to minimize the energy function of a Qtron NN.
Halftoning
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Image Halftoning
Graytone image ? halftone image can be formulated
as to minimize the energy function of a Qtron NN.
Halftoning
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The Qtron NN for Image Halftoning
Plane-G (Graytone image)
Plane-H (Halftone image)
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Image Halftoning
Plane-G (Graytone image)
Clamp-mode
Question
Answer
Free-mode
Plane-H (Halftone image)
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Image Restoration
Plane-G (Graytone image)
Free-mode
Answer
Question
Clamp-mode
Plane-H (Halftone image)
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Stacking Rule
The satisfaction of stacking rule can also be
formulated as to minimize the energy function of
a Qtron NN.
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Stacking Rule
The satisfaction of stacking rule can also be
formulated as to minimize the energy function of
a Qtron NN.
See the paper for the detail.
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The Total Energy
Total Energy
Image Halftoning
Stacking Rule
Share 1
Share 2
Share 1
Target
Target
Share 2
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The Qtron NN for VC/VA
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Application ? Visual Cryptography
Clamp-Mode
Free-Mode
Free-Mode
Free-Mode
Clamp-Mode
Clamp-Mode
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Application ? Visual Authorization
Key Share
User Share
Authority
VIP
IP
P
Key Share
User Share
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Application ? Visual Authorization
Producing key Share the first user share.
Key Share
User Share
Authority
Clamp-Mode
VIP
IP
P
Plane-GT
Free-Mode
Plane-HT
Free-Mode
Free-Mode
Plane-HS2
Plane-GS2
Clamp-Mode
Clamp-Mode
Key Share
User Share
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Application ? Visual Authorization
Producing other user shares.
Key Share
User Share
Authority
Clamp-Mode
VIP
IP
P
Plane-GT
Some are clamped and some are free.
Plane-HT
Clamp-Mode
Free-Mode
Plane-HS2
Plane-HS1
Plane-GS2
Plane-GS1
Clamp-Mode
Key Share
User Share
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Application ? Visual Authorization
Producing other user shares.
Key Share
User Share
Authority
Clamp-Mode
VIP
IP
P
Plane-GT
Some are clamped and some are free.
Plane-HT
Clamp-Mode
Free-Mode
Plane-HS2
Plane-HS1
Plane-GS2
Plane-GS1
Clamp-Mode
Key Share
User Share
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Application ? Visual Authorization
Key Share
User Share
Authority
Clamp-Mode
VIP
IP
P
Plane-GT
Some are clamped and some are free.
Plane-HT
Clamp-Mode
Free-Mode
Plane-HS2
Plane-HS1
Plane-GS2
Plane-GS1
Clamp-Mode
Key Share
User Share
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User Share
VIP
IP
User Share
Key Share
User Share
P
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A Neural-Network Approach for Visual Cryptography

• General
• Access Scheme

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Full Access Scheme ? 3 Shares
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Shares
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Full Access Scheme ? 3 Shares
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Shares
Theoretically, unrealizable. We did it in
practical sense.
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Full Access Scheme ? 3 Shares
S1
S2
S3
S1S2
S1S3
S2S3
S1S2S3
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Access Schemewith Forbidden Subset(s)
Anyone knows what is it?
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Access Schemewith Forbidden Subset(s)
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Shares
Theoretically, realizable.
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Access Schemewith Forbidden Subset(s)
S1
S2
S3
S1S2
S1S3
S2S3
S1S2S3
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A Neural-Network Approach for Visual Cryptography

• Conclusion

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Conclusion

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

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Thanks for Attention
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