Technical Points about Adaptive Steganography by Oracle (ASO)

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1 S. Kouider et al. (LIRMM, France) 1 / 23 EUSIPCO 2012 Technical Points about Adaptive Steganography by Oracle (ASO) Sarra Kouider, Marc Chaumont, William Puech firstname.surname@lirmm.fr kouider

2 S. Kouider et al. (LIRMM, France) 2 / 23 EUSIPCO 2012 Steganography vs Steganalysis Adaptive steganography Steganography vs Steganalysis

3 S. Kouider et al. (LIRMM, France) 3 / 23 EUSIPCO 2012 Steganography vs Steganalysis Adaptive steganography Adaptive steganography Goal Transmit m bits in a cover object X of n elements by making small perturbations. Solution Dening the embedding impact: D(X, Y) = X Y ρ= n i=1 ρ i x i y i. Find the stego object Y that minimizes the distortion function D under the constraint of the xed payload: Y = Emb(X, m) = arg min D(X, Y). HUGO [Pevný et al., IH 2010]. MOD [Filler et al., SPIE 2011].

Transmit m bits in a cover object X of n elements by making small perturbations.

4 S. Kouider et al. (LIRMM, France) 3 / 23 EUSIPCO 2012 Steganography vs Steganalysis Adaptive steganography Adaptive steganography Goal Transmit m bits in a cover object X of n elements by making small perturbations. Solution Dening the embedding impact: D(X, Y) = X Y ρ= n i=1 ρ i x i y i. Find the stego object Y that minimizes the distortion function D under the constraint of the xed payload: Y = Emb(X, m) = arg min D(X, Y). HUGO [Pevný et al., IH 2010]. MOD [Filler et al., SPIE 2011].

5 S. Kouider et al. (LIRMM, France) 4 / 23 EUSIPCO 2012 Steganography vs Steganalysis Adaptive steganography Outline

6 S. Kouider et al. (LIRMM, France) 5 / 23 EUSIPCO 2012 Embedding process ASO's design 1 2 Embedding process ASO's design 3 4 5

7 S. Kouider et al. (LIRMM, France) 6 / 23 EUSIPCO 2012 Embedding process ASO's design The Adaptive Steganography by Oracle (ASO).

8 S. Kouider et al. (LIRMM, France) 6 / 23 EUSIPCO 2012 Embedding process ASO's design

9 S. Kouider et al. (LIRMM, France) 7 / 23 EUSIPCO 2012 Embedding process ASO's design For each pixel (x i ) = ρ i = 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.

10 S. Kouider et al. (LIRMM, France) 8 / 23 EUSIPCO 2012 Embedding process ASO's design Our proposed approach : respectively for ρ ( ) i ρ (+) i = L l=1 ρ(l)(+) i

11 S. Kouider et al. (LIRMM, France) 9 / 23 EUSIPCO 2012 Embedding process ASO's design Our proposed approach : For each FLD classier (F l ) ρ (l)(+) i ρ (l)( ) i = w(l). (l)(+) (fx x fx (l) ) i s (l) = w(l). (l)( ) (fx x fx (l) ) i s (l) where f x (l) : Feature vector before modication. f x xi (l)(±) : Feature vector after a pixel modication ±1.

12 S. Kouider et al. (LIRMM, France) 10 / 23 EUSIPCO 2012 Embedding process ASO's design Embedding process

13 S. Kouider et al. (LIRMM, France) 11 / 23 EUSIPCO 2012 Embedding process ASO's design Embedding Process Dening the embedding impact: D(X, Y) = X Y ρ= n i=1 ρ i x i y i. Find the stego object Y that minimizes the distortion function D under the constraint of a xed payload: Y = Emb(X, m) = arg min D(X, Y). or Simulating the optimal embedding algorithm. Using the practical STC algorithm. T. Filler, J. Judas, and J. Fridrich Minimizing embedding impact in steganography using trellis-coded quantization. In SPIE. San Jose, CA, January 18-20, 2010.

14 S. Kouider et al. (LIRMM, France) 12 / 23 EUSIPCO 2012 Embedding process ASO's design ASO's design Oracle learns on 5000 covers and 5000 HUGO stego images from BOSSBase v1.00. Each image is represented by a vector of d = 5330 MINMAX features [Fridrich et al., 2011]. Personal implementation of the FLD ensemble classiers with d red = 30, and L = 30 classiers. Complexity reduction trick (from 2 years to 1.5 days) for 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.

BOSSBase v1.00. Each image is represented by a vector of d = 5330 MINMAX features [Fridrich et al., 2011].

15 S. Kouider et al. (LIRMM, France) 13 / 23 EUSIPCO 2012 The steganography by database paradigm Security measure The steganography by database paradigm Security measure 4 5

16 S. Kouider et al. (LIRMM, France) 14 / 23 EUSIPCO 2012 The steganography by database paradigm Security measure The steganography by database paradigm Paradigm: Requires a cover database at the input of the embedding process, instead of 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 dierent messages (batch steganography).

database paradigm Paradigm: Requires a cover database at the input of the embedding process, instead of just one

17 S. Kouider et al. (LIRMM, France) 15 / 23 EUSIPCO 2012 The steganography by database paradigm Security measure The proposed security measure high score S FLD high stego image security.

18 S. Kouider et al. (LIRMM, France) 16 / 23 EUSIPCO 2012 ASO's security performance Security measure performance ASO's security performance Security measure performance 5

19 ASO's security performance Security measure performance Evaluation protocol: ASO's security performance Blind steganalysis (ASO vs HUGO) Kodovský ensemble classier. BossBase v1.00 database with Rich Model SRMQ1 of features [Fridrich et al., 2012]. Detection Error: 1 P E = min (P FA + P MD (P FA )). P FA 2 J.J. Fridrich, and J. Kodovský Rich Models for steganalysis od Digital Images. In IEEE Transactions on Information Forensics and security S. Kouider et al. (LIRMM, France) 17 / 23 EUSIPCO 2012

, 2012]. Detection Error: 1 P E = min (P FA + P MD (P FA )). P FA 2 J.J. Fridrich, and J.

20 S. Kouider et al. (LIRMM, France) 18 / 23 EUSIPCO 2012 ASO's security performance Security measure performance ASO's security performance

21 S. Kouider et al. (LIRMM, France) 19 / 23 EUSIPCO 2012 ASO's security performance Security 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 S FLD security criterion.

22 S. Kouider et al. (LIRMM, France) 20 / 23 EUSIPCO 2012 ASO's security performance Security measure performance Security measure performance

23 S. Kouider et al. (LIRMM, France) 21 / 23 EUSIPCO

24 S. Kouider et al. (LIRMM, France) 22 / 23 EUSIPCO 2012 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.

25 S. Kouider et al. (LIRMM, France) 23 / 23 EUSIPCO 2012

Color Image Stegananalysis Using Correlations between RGB Channels

Color Image Stegananalysis Using Correlations between RGB Channels Hasan Abdulrahman, Marc Chaumont, Philippe Montesinos, Baptiste Magnier To cite this version: Hasan Abdulrahman, Marc Chaumont, Philippe