N-Secure Fingerprinting for Copyright Protection of Multimedia Won-gyum Kim 2004. 08. 23.
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Transcript of N-Secure Fingerprinting for Copyright Protection of Multimedia Won-gyum Kim 2004. 08. 23.
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Contents
• Watermarking vs. Fingerprinting• Collusion attacks• Collusion-secure fingerprinting code• N-secure fingerprinting code• Experimental results• Conclusion
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Watermarking vs. Fingerprinting
• Information hiding technique to protect copyright protection of multimedia
• Watermarking– Embed owner’s information– Protect owner’s copyright– Only one watermarked content
• Fingerprinting– Embed customer’s information– Trace customer who re-distributes contents
illegally– Many different fingerprinted contents
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Watermarking vs. Fingerprinting
Owner’sinformation
Content
Customer 1
Customer 2
• Watermarking– Distribute same contents
Customer N
... ......
O
O
O
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Watermarking vs. Fingerprinting
Customer’sinformation
Content
Customer 1
Customer 2
• Fingerprinting– Distribute different contents
Customer N
... ......
N
2
1
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Collusion attacks
• Use differences among fingerprinted contents– Averaging attack– Min-Max attack– Negative correlation attack– Zero-correlation attack
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Collusion Attack
• Averaging Attack– Average fingerprinted contents together
• Min-Max Attack– Average min and max value of the
fingerprinted contents
k
jkjj wK
dd ,
1'
2/)(' min,max, jjjj wwdd
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Collusion Attack
• Negative-correlation attack– Use median value
• Zero-correlation attack– Use a target fingerprinted content to compare
otherwisew
wwwwd
j
jjmedjj
j,
)1(,
min,
min,max,,max,
otherwisew
wwwwd
j
jjjTj
j
,
)(2
1,
min,
min,max,,max,
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Collusion-secure FC
• Marking Assumption– By colluding, users can detect a specific
mark if it differs between their copies; otherwise a mark can not be detected.
• The aim of collusion-secure FC– After colluding, identify all colluders or
at least more than one colluder
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Collusion-secure FC
• To make robust to collusion– After colluding, the location of detectible
code is unique according to all combinations of collusion
• Basic idea– For 3 customers and 2 colluders
C1 : 1 0 1 Collude C1 & C2 : 1 0 0
C2 : 1 1 0 Collude C2 & C3 : 0 1 0
C3 : 0 1 1 Collude C1 & C3 : 0 0 1
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Collusion-secure FC
• 2-detecting code (Dittman, 2000)– Based on the finite projective space
– Code for 3 customers with 2 colluders• C1 : 1 0 0 0 1 1 0• C2 : 1 1 1 0 0 0 0• C3 : 0 0 1 1 1 0 0
Fingerprint 1Fingerprint 2
Fingerprint 3
Points
Lines
1
26 7
345
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N-secure FC
• Use the location of undetectable code
• Content ID + Customer ID• Code length is N+1
Otherwise
indexcustomernifC
indexcustomeriCCCC
n
ni
,1
)(,0
},,......,,,1{ 21
Content IDCustomer ID
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N-secure FC
• Code example– For 7 customers with 7 colluders
C1 : 1 0 1 1 1 1 1 1C2 : 1 1 0 1 1 1 1 1C3 : 1 1 1 0 1 1 1 1C4 : 1 1 1 1 0 1 1 1C5 : 1 1 1 1 1 0 1 1C6 : 1 1 1 1 1 1 0 1C7 : 1 1 1 1 1 1 1 0
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N-secure FC
• Collusions C1 & C2 : 1 X X 1 1 1 1 1 C2 & C5 & C7 : 1 1 X 1 1 X 1 XC3 & C4 & C5 & C6 : 1 1 1 X X X X 1
All customers : 1 X X X X X X X
X : undetectable
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Embedding
CodeGenerator
Key
Customerindex
FingerprintedImage
W
Original ImageMF(α)
HVS
Shuffle
Produce pattern
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Experimental Results
• 512x512 Gray-scale Lena image• For 15 users with 15 colluders• Use general watermarking scheme• Embed fingerprinting code into spatial domain of the image• Divide into blocks and use shuffling to improve security
level• Do not consider the other watermarking attacks
(a) Customer 1 without collusion (b) Collude Customer 1 & 2 (c) Collude all customers (1…15)