a neural-network approach for visual cryptography 虞台文 大同大學資工所
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A Neural-Network Approach for Visual Cryptography Overview 大同大學資工所TRANSCRIPT
A Neural-Network Approach for Visual Cryptography
虞台文大同大學資工所
Content Overview The Q’tron NN Model The Q’tron NN Approach for
– Visual Cryptography– Visual Authorization– Semipublic Encryption
General Access Scheme Conclusion
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.
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
The Basic Concept of VC
Target Image(The Secret)
Share 2
Share 1AccessScheme
The (2, 2) access scheme.
The Shares Produced by NN
Target Image(The Secret)
Share 2
Share 1NeuralNetwork
We get shares after the NN settles down.
Decrypting Using Eyes
Share 2
Share 1
Example: (2, 2)
Target image
Share image2
Share image1
Plane shares are used
Traditional Approach Naor and Shamir (2,2)
Pixel Probability Shares#1 #2
Superposition ofthe two shares
5.0p
5.0p
5.0p
5.0p
WhitePixels
BlackPixels
The Code Book
The VA Scheme
keyshare
user shares(resource 2)
user shares(resource 1)
stacking
stacking
…
…VIP IP P
…VIP IP P
Very Important Person.
…
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
public share(database in lab)
AB CD XY UV
stacking
usershares
keys
素貞
The SE Scheme
循鋰 美靜 作中
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
A Neural-Network Approach for Visual Cryptography
The Q’tron NN Model
大同大學資工所
The Q’tron
i
(ai )
. . .
0 1 2 qi1
aiQiActive value
Qi{0, 1, …, qi1}IiRExternal Stimulus
( )ij j jj
T a QInternal Stimulus
Ni
Noise
Quantum Neuron
The Q’tron
i
(ai )
. . .
0 1 2 qi1
aiQiActive value
Qi{0, 1, …, qi1}IiRExternal Stimulus
( )ij j jj
T a QInternal Stimulus
Ni
Noise
Free-Mode Q’tron
The Q’tron
i
(ai )
. . .
0 1 2 qi1
aiQiActive value
Qi{0, 1, …, qi1}IiRExternal Stimulus
( )ij j jj
T a QInternal Stimulus
Ni
Noise
Clamp-Mode Q’tron
Input Stimulus
InternalStimulus
ExternalStimulus
Noise
NoiseFreeTerm
i
(ai )
. . .
Noise
Level Transition
Running Asynchronously
i
(ai )
. . .
Energy Function
InteractionAmong Q’tronsInteraction
withExternal Stimuli
ConstantMonotonically Nonincreasing
The Q’tron NN
Interface/Hidden Q’trons clamp-modefree-modefree mode Hidden Q’trons
Interface Q’trons
Question-Answering
Feed a question by clamping some interface Q’trons.
clamp-modefree-modefree mode Hidden Q’trons
Interface Q’trons
Question-AnsweringRead answer when all interface Q’trons settle down.
clamp-modefree-modefree mode Hidden Q’trons
Interface Q’trons
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
Energy Function for VC
Visual CryptographyImage Halftoning Image
Stacking
+
Image HalftoningGraytone 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.
Image HalftoningGraytone 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.
The Q’tron NN for Image Halftoning
Plane-G (Graytone image)
Plane-H (Halftone image)
Image Halftoning
HalftoningClamp-mode
Free-mode
Plane-G (Graytone image)
Plane-H (Halftone image)
Question
Answer
Image RestorationPlane-G (Graytone image)
Plane-H (Halftone image)
Restoration
Clamp-mode
Free-mode
Question
Answer
Stacking Rule
+ + + +
The satisfaction of stacking rule can also be formulated as to minimize the energy function of a Q’tron NN.
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.
The Total Energy
+
Share 1 Target
Share 1
Share 2
TargetShare 2
TotalEnergy
Image Halftoning Stacking Rule
The Q’tron NN for VC/VA
Plane-GS1
Plane-HS1
Share 1
Plane-HS2
Plane-GS2
Share 2
Plane-GT
Plane-HT
Target
Application Visual Cryptography
Plane-GS1
Plane-HS1
Share 1
Plane-HS2
Plane-GS2
Share 2
Plane-GT
Plane-HT
Target
Clamp-Mode
Clamp-Mode
Clamp-Mode
Free-Mode Free-Mode
Free-Mode
Application Visual Authorization
Plane-GS1
Plane-HS1
User Share
Authority
Plane-HS2
Plane-GS2
Plane-GT
Plane-HT
Key Share
Key Share
User Share
VIP IP P
Application Visual Authorization
Plane-GS1
Plane-HS1
User Share
Authority
Clamp-Mode
Free-ModePlane-HS2
Plane-GS2Clamp-Mode
Free-Mode
Plane-GT
Plane-HT
Clamp-Mode
Free-Mode
Key Share
Key Share
User Share
VIP IP P
Producing key Share & the first user share.
Application Visual Authorization
Plane-GS1
Plane-HS1
User Share
Authority
Clamp-ModePlane-HS2
Plane-GS2Clamp-Mode
Free-Mode
Plane-GT
Plane-HT
Clamp-Mode
Some are clamped and some are free.
Key Share
Key Share
User Share
VIP IP P
Producing other user shares.
Application Visual Authorization
Plane-GS1
Plane-HS1
User Share
Authority
Clamp-ModePlane-HS2
Plane-GS2Clamp-Mode
Free-Mode
Plane-GT
Plane-HT
Clamp-Mode
Some are clamped and some are free.
Key Share
Key Share
User Share
VIP IP P
Producing other user shares.
Application Visual Authorization
Plane-GS1
Plane-HS1
User Share
Authority
Clamp-ModePlane-HS2
Plane-GS2Clamp-Mode
Free-Mode
Plane-GT
Plane-HT
Clamp-Mode
Some are clamped and some are free.
Key Share
Key Share
User Share
VIP IP P
Key Share
User Shar
e
User Shar
e
User Shar
e
VIP
IP
P
A Neural-Network Approach for Visual Cryptography
GeneralAccess Scheme
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Full Access Scheme 3 Shares
朝辭白帝彩雲間朝 辭 白
帝 彩 雲
間Shares
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.
Full Access Scheme 3 Shares
S1 S2 S3
S1+S2 S1+S3 S2+S3 S1+S2+S3
Access Schemewith Forbidden Subset(s)
Anyone knows what is it?
Access Schemewith Forbidden Subset(s)
人之初性本善人 之 初
性 本 X
善
Theoretically,
realizable.
Shares
Access Schemewith Forbidden Subset(s)
S1 S2 S3
S1+S2 S1+S3 S2+S3 S1+S2+S3
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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