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A novel secret image sharing
scheme for true-color images
with size constraint
Source: Information Sciences, Vol. 179, No. 19,
Sep.2009,pp.3247-3254.
Author: Tsai Du-Shiau, Horng Gwoboa,
Chen Tzung-Her, Huang Yao-Te
Article history:
Received 2 August 2008
Received in revised form 21 May 2009
Accepted 29 May 2009
Advisor: Chih-Hung Lin
Speaker: 趙家寬
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Outline
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Introduction
Review of related works
The proposed scheme
Experimental results
Discussion
Conclusions
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Introduction(1/3)
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Secret Image Sharing (SIS)
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1.
2.
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Distributing partial information, called shares
Two types of SIS
Lossless
Lossy
Function
f ()
X i '  f ( X i ) ; and
X i  f 1 ( f ( X i )) .
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Introduction(2/3)
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Three main categories of SIS
VC-based(視覺密碼學)
2. VVSS-based (變異視覺式機密分享)
3. IM-based(插值方法)
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Introduction(3/3)
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Four guidelines to propose a new SIS scheme
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Support 24-bit true-color image
Size of each share is slightly increased
Camouflage images for hiding secrets should be
natural
Visual perception and image properties
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Review of related works(1/7)
1.
VC-based secret image sharing schemes
(Proposed by Naor and Shamir)
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Review of related works(2/7)
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Characteristics
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Perfectly secure
Decode secrets by visual systems with no
cryptography knowledge and without performing
cryptographic computation
Transparencies are meaningless
Size of each transparency is much larger than that
of secret images
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Review of related works(3/7)
VVSS-based secret image sharing schemes
(Proposed by Chang et al.)
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Give three n x m pixels image - C-color secret image S and
two randomly chosen cover images O1 and O2
Colors of S is stored in the Color Index Table (CIT)
Each pixel in O1 and O2 is expanded into minimized
M  t  t subpixels to obtain two camouflage images, O1’
and O2’
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Review of related works(4/7)
M satisfies :
(3)
(4)
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Review of related works(5/7)
Bit-level based SIS scheme
(Proposed by Lukac and Plataniotis)
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Review of related works(6/7)
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PCA and FFN neural networks
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Principal component analysis (PCA)
Color image compression scheme with hybrid
neural network consisting of Sanger’s
algorithm
(Proposed by Clausen and Wechsler)
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Review of related works(7/7)
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The neural network begins with initial A and
learns/updates weights aij according to
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A feed forward neural network trained with back
propagation also begins with initial B and learns/updates
weights bij according to
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The proposed scheme(1/4)
1)
Initial phase
Two global weighted matrices A and B
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The proposed scheme(2/4)
Encoding phase
2)
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Input: a true-color secret image X
Output: n camouflage images
(11)
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The proposed scheme(3/4)
(12)
(13)
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The proposed scheme(4/4)
Decoding phase
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Input: n camouflage images, global weighted
matrix B, seed s
Output: true-color secret image X’
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Experimental results
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Discussion(1/2)
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Support the 24-bit true-color secret image
Size constraint on shares
Quality improvement
Efficiency of bandwidth and storage space
Security enhancement
Low computational power
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Discussion(2/2)
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