echnicalT Points about Adaptive Steganography by · PDF fileechnicalT Points about Adaptive...
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IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
Technical Points about Adaptive Steganography by Oracle (ASO)
Sarra Kouider, Marc Chaumont, William Puech
E-mail: [email protected]
http://www.lirmm.fr/∼kouider
S. Kouider et al. (LIRMM, France) 1 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
Steganography vs SteganalysisAdaptive steganography
Steganography vs Steganalysis
S. Kouider et al. (LIRMM, France) 2 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
Steganography vs SteganalysisAdaptive steganography
Adaptive steganography
Goal
Transmit m bits in a cover object X of n elements by making small perturbations.
Solution
De�ning the embedding impact:D(X,Y) =‖ X− Y ‖ρ=
∑ni=1 ρi | xi − yi | .
Find the stego object Y that minimizes the distortion function D underthe constraint of the �xed payload: Y = Emb(X,m) = argminD(X,Y).
⇒ HUGO [Pevný et al., IH 2010]. ⇒ MOD [Filler et al., SPIE 2011].
S. Kouider et al. (LIRMM, France) 3 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
Steganography vs SteganalysisAdaptive steganography
Adaptive steganography
Goal
Transmit m bits in a cover object X of n elements by making small perturbations.
Solution
De�ning the embedding impact:D(X,Y) =‖ X− Y ‖ρ=
∑ni=1 ρi | xi − yi | .
Find the stego object Y that minimizes the distortion function D underthe constraint of the �xed payload: Y = Emb(X,m) = argminD(X,Y).
⇒ HUGO [Pevný et al., IH 2010]. ⇒ MOD [Filler et al., SPIE 2011].
S. Kouider et al. (LIRMM, France) 3 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
Steganography vs SteganalysisAdaptive steganography
Outline
2 The proposed ASO scheme
3 Steganography by database
4 Experimental results
5 Conclusion
S. Kouider et al. (LIRMM, France) 4 / 23 EUSIPCO 2012
1 Introduction
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
1 Introduction
2 The proposed ASO scheme
The detectability map computation
Embedding process
ASO's design
3 Steganography by database
4 Experimental results
5 Conclusion
S. Kouider et al. (LIRMM, France) 5 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
The proposed ASO scheme
The Adaptive Steganography by Oracle (ASO).
S. Kouider et al. (LIRMM, France) 6 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
The detectability map computation
S. Kouider et al. (LIRMM, France) 6 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
The detectability map computation
For each pixel (xi ) =⇒ ρi = min(ρ(+)i , ρ
(−)i ) .
T. Pevný, T.Filler, and P. Bas
Using High-Dimensional Image Models to perform Highly Undetectable Steganography. In IH'12th
International Workshop. LNCS. Calgary, Canada. June 28-30, 2010.
S. Kouider et al. (LIRMM, France) 7 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
The detectability map computation
Our proposed approach :
ρ(+)i =
∑Ll=1 ρ
(l)(+)i
respectively for ρ(−)i
S. Kouider et al. (LIRMM, France) 8 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
The detectability map computation
Our proposed approach :
For each FLD classi�er (Fl )
ρ(l)(+)i =
w(l).
(fx∼x
i
(l)(+)−fx(l)
)s(l)
ρ(l)(−)i =
w(l).
(fx∼x
i
(l)(−)−fx(l)
)s(l)
where
fx(l): Feature vector before modi�cation.
fx∼xi
(l)(±): Feature vector after a pixel modi�cation ±1.
S. Kouider et al. (LIRMM, France) 9 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
Embedding process
S. Kouider et al. (LIRMM, France) 10 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
Embedding Process
De�ning the embedding impact:D(X,Y) =‖ X− Y ‖ρ=
∑ni=1 ρi | xi − yi | .
Find the stego object Y that minimizes the distortion function D underthe constraint of a �xed payload: Y = Emb(X,m) = argminD(X,Y).
⇒ Simulating the optimal embedding algorithm.
or⇒ Using the practical STC algorithm.
T. Filler, J. Judas, and J. Fridrich
Minimizing embedding impact in steganography using trellis-coded quantization. In SPIE. SanJose, CA, January 18-20, 2010.
S. Kouider et al. (LIRMM, France) 11 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The detectability map computationEmbedding processASO's design
ASO's design
Oracle learns on 5000 covers and 5000 HUGOstego images from BOSSBase v1.00.
Each image is represented by a vector ofd = 5330 MINMAX features [Fridrich et al., 2011].
Personal implementation of the FLD ensembleclassi�ers with dred = 30, and L = 30 classi�ers.
Complexity reduction trick (from 2 years to 1.5days) for 10000 images.
General scheme of ASO.
J. Fridrich, Kodovský, V. Holub, and M. Goljan
Breaking HUGO - the Process Discovery. In IH. Prague, Czech Republic, May 18-20, 2011.
S. Kouider et al. (LIRMM, France) 12 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The steganography by database paradigmSecurity measure
1 Introduction
2 The proposed ASO scheme
3 Steganography by database
The steganography by database paradigm
Security measure
4 Experimental results
5 Conclusion
S. Kouider et al. (LIRMM, France) 13 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The steganography by database paradigmSecurity measure
The steganography by database paradigm
Paradigm:
Requires a cover database at the input of the embedding process, insteadof just one image.
Preserves both cover image and sender's database distributions.
May output:
One stego images with the secret message (one-time database).
Or multiple stego images with di�erent messages (batch steganography).
S. Kouider et al. (LIRMM, France) 14 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
The steganography by database paradigmSecurity measure
The proposed security measure
high score SFLD ⇒ high stego image security.
S. Kouider et al. (LIRMM, France) 15 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
ASO's security performanceSecurity measure performance
1 Introduction
2 The proposed ASO scheme
3 Steganography by database
4 Experimental results
ASO's security performance
Security measure performance
5 Conclusion
S. Kouider et al. (LIRMM, France) 16 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
ASO's security performanceSecurity measure performance
Evaluation protocol: ASO's security performance
Blind steganalysis (ASO vs HUGO)
• Kodovský ensemble classi�er.
• BossBase v1.00 database with 10000 512× 512.
• Rich Model SRMQ1 of 12753 features [Fridrich et al., 2012].
Detection Error:
PE = minPFA
1
2(PFA + PMD (PFA)) .
J.J. Fridrich, and J. Kodovský
Rich Models for steganalysis od Digital Images. In IEEE Transactions on Information Forensics
and security. 2012.
S. Kouider et al. (LIRMM, France) 17 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
ASO's security performanceSecurity measure performance
ASO's security performance
S. Kouider et al. (LIRMM, France) 18 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
ASO's security performanceSecurity measure performance
Evaluation protocol: Security measure performance
OC-SVM machine learning with Gaussian kernel.
Learning phase conducted on BossBase v1.00 cover images.
B(α)1 : 500 randomly selected ASO's stego images.
B(α)2
: 500 selected ASO's stego images using the SFLD securitycriterion.
S. Kouider et al. (LIRMM, France) 19 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
ASO's security performanceSecurity measure performance
Security measure performance
S. Kouider et al. (LIRMM, France) 20 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
1 Introduction
2 The proposed ASO scheme
3 Steganography by database
4 Experimental results
5 Conclusion
S. Kouider et al. (LIRMM, France) 21 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
Conclusion
Summary
A new secure adaptive embedding algorithm: ASO.
Presentation of the steganography by database paradigm.
A selection criterion for the stego images.
Future work
Security evaluation with a pooled steganalysis.
Other security criterion.
Position with game theory aspects.
S. Kouider et al. (LIRMM, France) 22 / 23 EUSIPCO 2012
IntroductionThe proposed ASO schemeSteganography by database
Experimental resultsConclusion
S. Kouider et al. (LIRMM, France) 23 / 23 EUSIPCO 2012