Transcript Folie 1 - uni

Multiple
Re-Watermarking
Scenarios
Severin Kampl, Daniel Mark
„Multiple Re-Watermarking“ Team
Carinthia Tech Institute,
University of Applied Sciences, Austria
Department of Computer Sciences,
Salzburg University, Austria
Michael Dorfer
Severin Kampl
Alexander Maier
Daniel Mark
Andreas Palli
Günter Scheer
Univ.-Prof. Mag. Dr. Andreas Uhl
Multiple Re-Watermarking Scenarios
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Presentation Structure
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Introduction
Multiple Re-Watermarking
Experimental Study
Conclusion and Perspectives
Multiple Re-Watermarking Scenarios
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Introduction
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- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
DRM & multimedia security
Significantly different properties of
Algorithms:
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Fragility (integrity investigations)
Robustness (ownership claims)
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Multiple Watermarking
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Composite watermarking
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One single embedding process
Segmented watermarking
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- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
Host data is partitioned
Successive watermarking (Re-Watermarking)
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Embedding of one watermark after the other
Our focus: Multiple Re-Watermarking with robust
techniques
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Target Scenario
owner info
recipient info
}
1st sale
re-sale
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
re-sale . . . . . .
host image
embedding technique
Scenario for reconstruction of the trading chain
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Watermark Detection
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- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
Non-Blind Algorithm
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Correct reference image is required
Blind
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Algorithm
No reference image is required
Result: Correlation value of (e.g. B with B‘)
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Experimental Study: Setting
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„Lena“ Image (512 x 512 Px, 8 bpp)
Freely available watermarking toolbox
Algorithms:
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- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
Wang (non-blind, waveletbased, MF - HF)
Corvi (non-blind, waveletbased NF - MF)
Koch (blind, DCT-based, random blocks)
Final PSNR >= 38db
Lena image, 512x512 Pixels, 8bpp
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WANG - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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WANG - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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WANG - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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WANG - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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WANG - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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CORVI - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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CORVI - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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WANG  CORVI
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- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
Explanation:
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Wang: most significant wavelet coefficients
 always different coefficients
Corvi: all approximation subband coefficients
 less overwriting
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KOCH - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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KOCH - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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KOCH - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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KOCH - Algorithm
Multiple Re-Watermarking Scenarios
- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion / Perspectives
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Conclusion & Perspectives
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- Introduction
- Multiple Re- Watermarking
- Experimental Study
- Settings & Methods
- Results
- Conclusion & Perspectives
WANG & KOCH as predicted
CORVI as predicted when using correct
ref. Img.
Corvi also useable in „blind“ way
Large number of WMs detectable
Robustness concerning compression
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Thank you for your Attentention!
Michael Dorfer, Severin Kampl, Alexander Maier, Daniel Mark,
Andreas Palli, Günter Scheer, Univ.-Prof. Mag. Dr. Andreas Uhl