1. N-Secure Fingerprinting for Copyright Protection of Multimedia
Won-gyum Kim

2. Contents Watermarking vs. Fingerprinting Collusion attacks
Collusion-secure fingerprinting code
N-secure fingerprinting code
Experimental results
Conclusion

3. 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

4. Watermarking vs. Fingerprinting
Distribute same contents O
Customer 1
Content O
Customer 2 . . .
Owner’s
information O
Customer N

5. Watermarking vs. Fingerprinting
Distribute different contents 1
Customer 1
Content 2
Customer 2 . . .
Customer’s
information N
Customer N

6. Collusion attacks Use differences among fingerprinted contents
Averaging attack
Min-Max attack
Negative correlation attack
Zero-correlation attack

7. Collusion Attack Averaging Attack Min-Max Attack
Average fingerprinted contents together
Min-Max Attack
Average min and max value of the fingerprinted contents
Embedding and retrieving in the frequency domain are similar to that in the spatial domain, but it is different in the point that the watermark is embedded in the frequency domain after FFT.
At first, transform the original image into the frequency domain using FFT
Next, multiply bit sequence generated by secret key with seal image and embed the results to amplitude value Then recover original image using inverse FFT.
Retrieving process is also similar to that in the embedding process.
Firstly, transform watermarked image into the frequency domain using FFT and estimate approximate original from amplitude value.
Then finally, extract seal image by multiplying the difference value with bit sequence generated by secret key.

8. Averaging attack ………… N Averaging image 1 2
As a second experiment, above is the result of embedding and extracting seal image in the frequency domain.

9. Collusion Attack Negative-correlation attack Zero-correlation attack
Use median value
Zero-correlation attack
Use a target fingerprinted content to compare

10. Collusion-secure FC Marking Assumption The aim of collusion-secure FC
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

11. Collusion-secure FC To make robust to collusion Basic idea
After colluding, the location of detectible code is unique according to all combinations of collusion
Basic idea
For 3 customers and 2 colluders
C1 : Collude C1 & C2 : 1 0 0
C2 : Collude C2 & C3 : 0 1 0
C3 : Collude C1 & C3 : 0 0 1

12. Collusion-secure FC 2-detecting code (Dittman, 2000)
Based on the finite projective space
Code for 3 customers with 2 colluders
C1 :
C2 :
C3 :
Fingerprint 1
Fingerprint 2
Fingerprint 3
Points
Lines 1 2 6 7 5 4 3

13. N-secure FC Use the location of undetectable code
Content ID + Customer ID
Code length is N+1
Content ID
Customer ID

14. N-secure FC Code example For 7 customers with 7 colluders

15. All customers : 1 X X X X X X X
N-secure FC
Collusions
X : undetectable
C1 & C2 : 1 X X
C2 & C5 & C7 : X X 1 X
C3 & C4 & C5 & C6 : X X X X 1
All customers : 1 X X X X X X X

16. Embedding HVS MF(α) Original Image Fingerprinted Image W Code
Customer
index
Code
Generator
Shuffle
Key
Produce pattern

17. Extracting Fingerprinted Image Correlator Filter De-shuffle Key
Customer
index

18. 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)

19. Conclusion
Proposed fingerprinting code robust to collusion attack of N customers
Trace user(s), who joined collusion
Content ID + Customer ID
Code length is N+1
Further study
Consider the other watermarking attacks
JPEG, RST, Filtering...
Reduce code length