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Steganalysis in Technicolor Boosting WS Detection of Stego Images from CFA-Interpolated Covers Matthias Kirchner and Rainer Böhme University of Münster IEEE ICASSP | Florence, Italy | May 8, 2014 WESTFÄLISCHE WILHELMS-UNIVERSITÄT MÜNSTER
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Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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Page 1: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

Steganalysis in TechnicolorBoosting WS Detection of Stego Images from CFA-Interpolated Covers

Matthias Kirchner and Rainer BöhmeUniversity of Münster

IEEE ICASSP | Florence, Italy |May 8, 2014

WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER

Page 2: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 2 /16

Are Steganographers Colorblind?

I research community seems to live in a monochromatic world wheregrayscale images abound

I a more colorful world raises questions about steganographic security

1 how plausible are grayscale images?2 how much can steganalysts gain from color information?

I this work: WS steganalysis and bilinear CFA interpolation

Matthias Kirchner

Page 3: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 2 /16

Are Steganographers Colorblind?

I research community seems to live in a monochromatic world wheregrayscale images abound

I a more colorful world raises questions about steganographic security

1 how plausible are grayscale images?2 how much can steganalysts gain from color information?

I this work: WS steganalysis and bilinear CFA interpolation

Matthias Kirchner

Page 4: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 2 /16

Are Steganographers Colorblind?

I research community seems to live in a monochromatic world wheregrayscale images abound

I a more colorful world raises questions about steganographic security

1 how plausible are grayscale images?

2 how much can steganalysts gain from color information?

I this work: WS steganalysis and bilinear CFA interpolation

Matthias Kirchner

Page 5: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 2 /16

Are Steganographers Colorblind?

I research community seems to live in a monochromatic world wheregrayscale images abound

I a more colorful world raises questions about steganographic security

1 how plausible are grayscale images?2 how much can steganalysts gain from color information?

I this work: WS steganalysis and bilinear CFA interpolation

Matthias Kirchner

Page 6: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 2 /16

Are Steganographers Colorblind?

I research community seems to live in a monochromatic world wheregrayscale images abound

I a more colorful world raises questions about steganographic security

1 how plausible are grayscale images?2 how much can steganalysts gain from color information?

I this work: WS steganalysis and bilinear CFA interpolation

Matthias Kirchner

Page 7: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 3 /16

WS Steganalysis [Fridrich & Goljan, 2004; Ker & Böhme, 2008]

I estimates the embedding rate p̂ of uniform LSB replacement embedding ingrayscale images

p̂ =2n

n∑i=1

(−1) x(p)i

(x(p)i − x̂(0)

i

) x (0) cover object ∈ Zn

x (p) stego object ∈ Zn

x̂ (0) cover estimatep embedding rate

w vector of weights

I cover estimate: linear prediction from spatial neighboorhood

FKB8 :

− 14

12 −

14

12 0 1

2− 1

412 −

14

FLS8 :

b a ba 0 ab a b

I enhanced variants for various assumptions about cover source andembedding strategies

Matthias Kirchner

Page 8: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 3 /16

WS Steganalysis [Fridrich & Goljan, 2004; Ker & Böhme, 2008]

I estimates the embedding rate p̂ of uniform LSB replacement embedding ingrayscale images

p̂ =2n

n∑i=1

(−1) x(p)i

(x(p)i − x̂(0)

i

) x (0) cover object ∈ Zn

x (p) stego object ∈ Zn

x̂ (0) cover estimatep embedding rate

w vector of weights

I cover estimate: linear prediction from spatial neighboorhood

FKB8 :

− 14

12 −

14

12 0 1

2− 1

412 −

14

FLS8 :

b a ba 0 ab a b

I enhanced variants for various assumptions about cover source andembedding strategies

Matthias Kirchner

Page 9: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 3 /16

WS Steganalysis [Fridrich & Goljan, 2004; Ker & Böhme, 2008]

I estimates the embedding rate p̂ of uniform LSB replacement embedding ingrayscale images

p̂ =2n

n∑i=1

wi (−1) x(p)i

(x(p)i − x̂(0)

i

) x (0) cover object ∈ Zn

x (p) stego object ∈ Zn

x̂ (0) cover estimatep embedding ratew vector of weights

I cover estimate: linear prediction from spatial neighboorhood

FKB8 :

− 14

12 −

14

12 0 1

2− 1

412 −

14

FLS8 :

b a ba 0 ab a b

I enhanced variants for various assumptions about cover source andembedding strategies

Matthias Kirchner

Page 10: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 4 /16

CFA InterpolationI typical digital images are captured with a color filter array

Bayer pattern

red channel green channel blue channel

I at least 2/3 of all pixels are interpolated

I intra-channel and inter-channel dependencies

Fgreen :

0 14 0

14 1 1

40 1

4 0

Fred :

14

12

14

12 1 1

214

12

14

bilinear interpolation:

Matthias Kirchner

Page 11: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 4 /16

CFA InterpolationI typical digital images are captured with a color filter array

Bayer pattern

red channel green channel blue channel

I at least 2/3 of all pixels are interpolated

I intra-channel and inter-channel dependencies

Fgreen :

0 14 0

14 1 1

40 1

4 0

Fred :

14

12

14

12 1 1

214

12

14

bilinear interpolation:

Matthias Kirchner

Page 12: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 4 /16

CFA InterpolationI typical digital images are captured with a color filter array

Bayer pattern

red channel green channel blue channel

I at least 2/3 of all pixels are interpolated

I intra-channel and inter-channel dependencies

Fgreen :

0 14 0

14 1 1

40 1

4 0

Fred :

14

12

14

12 1 1

214

12

14

bilinear interpolation:

Matthias Kirchner

Page 13: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 5 /16

Spatial Neighborhood Categories

I CFA configuration defines characteristic pixel neighborhood categories

I x{C} = (xi | i ∈ C) C ∈ {R, 4N, 4D, 2H, 2V}

Bayer pattern green channel red channel

“raw” interpolated

4N

4D

2H

2V

Matthias Kirchner

Page 14: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 5 /16

Spatial Neighborhood Categories

I CFA configuration defines characteristic pixel neighborhood categoriesI x{C} = (xi | i ∈ C) C ∈ {R, 4N, 4D, 2H, 2V}

Bayer pattern green channel red channel

“raw”

interpolated

4N

4D

2H

2V

Matthias Kirchner

Page 15: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 6 /16

Spatial Neighborhood PredictabilityI OLS linear prediction from all 3× 3 neighborhoods (FLS8 ) per category

I prediction error

4N R global

0

2

4

6

8

10

12

RMS

I estimated coefficients

14

12

34

dire

ctne

ighb

ors

4N R global

− 12

− 14

0

diag

onal

neig

hbor

s

bilinear interpolation, green channel,7408 images (size: 512×512)

4N R 4N R 4N

R 4N R 4N R

4N R 4N R 4N

R 4N R 4N R

4N R 4N R 4N

“perfect” predictabilityof interpolated pixels

Matthias Kirchner

Page 16: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 6 /16

Spatial Neighborhood PredictabilityI OLS linear prediction from all 3× 3 neighborhoods (FLS8 ) per category

I prediction error

4N R global

0

2

4

6

8

10

12

RMS

I estimated coefficients

14

12

34

dire

ctne

ighb

ors

4N R global

− 12

− 14

0

diag

onal

neig

hbor

s

bilinear interpolation, green channel,7408 images (size: 512×512)

4N R 4N R 4N

R 4N R 4N R

4N R 4N R 4N

R 4N R 4N R

4N R 4N R 4N

“perfect” predictabilityof interpolated pixels

Matthias Kirchner

Page 17: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 6 /16

Spatial Neighborhood PredictabilityI OLS linear prediction from all 3× 3 neighborhoods (FLS8 ) per category

I prediction error

4N R global

0

2

4

6

8

10

12

RMS

I estimated coefficients

14

12

34

dire

ctne

ighb

ors

4N R global

− 12

− 14

0

diag

onal

neig

hbor

s

bilinear interpolation, green channel,7408 images (size: 512×512)

4N R 4N R 4N

R 4N R 4N R

4N R 4N R 4N

R 4N R 4N R

4N R 4N R 4N

“perfect” predictabilityof interpolated pixels

Matthias Kirchner

Page 18: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 7 /16

CFA-Inspired Linear Pixel Predictors

I different neighborhood categories, C, call for tailored predictors

option 1: estimated from image per category, FLS8

(x (p){C}

)

option 2: fixed pre-set filter kernels, FC(x (p){C}

)

green channel red channel

R4N

4D

2H

2V

Matthias Kirchner

Page 19: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 7 /16

CFA-Inspired Linear Pixel Predictors

I different neighborhood categories, C, call for tailored predictors

option 1: estimated from image per category, FLS8

(x (p){C}

)option 2: fixed pre-set filter kernels, FC

(x (p){C}

)

green channel red channel

R4N

4D

2H

2V

F4N

0 14 0

14 0 1

40 1

4 0

F4D

14 0 1

40 0 014 0 1

4

F2H

0 0 012 0 1

20 0 0

F2V

0 12 0

0 0 00 1

2 0

bilinear interpolation:

Matthias Kirchner

Page 20: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 8 /16

CFA-WS Steganalysis

I utilization of CFA neighborhood relations in individual color channels

p̂C =2|{C}|

∑{i∈C}

(−1) x(p)i

(x(p)i −FC

(x (p))

i

) x (p) stego object ∈ Zn

FC predictor for categoryC ∈ {R, 4N, 4D, 2H, 2V}

p embedding rate

w vector of weights

green channel red channel

R4N

4D

2H

2V

I aggregation to a combinedestimate p̂ is equivalent toassigning weights

Matthias Kirchner

Page 21: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 8 /16

CFA-WS Steganalysis

I utilization of CFA neighborhood relations in individual color channels

p̂C =2|{C}|

∑{i∈C}

wi (−1) x(p)i

(x(p)i −FC

(x (p))

i

) x (p) stego object ∈ Zn

FC predictor for categoryC ∈ {R, 4N, 4D, 2H, 2V}

p embedding ratew vector of weights

green channel red channel

R4N

4D

2H

2V

I aggregation to a combinedestimate p̂ is equivalent toassigning weights

Matthias Kirchner

Page 22: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 9 /16

Experimental Setup

I 10, 000 BOSSBase grayscale images (size: 512× 512), sampled onto aBayer grid to apply plain bilinear CFA interpolation

I 3, 400 raw color image patches (size: 512× 512) from the Dresden ImageDatabase (Nikon D70), each processed with dcraw bilinear interpolationand proprietary content-adaptive Adobe Lightroom interpolation

I uniform LSB embedding

I exclude covers with >5 % flat blocks (size: 3× 3 )

Matthias Kirchner

Page 23: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 9 /16

Experimental Setup

I 10, 000 BOSSBase grayscale images (size: 512× 512), sampled onto aBayer grid to apply plain bilinear CFA interpolation

I 3, 400 raw color image patches (size: 512× 512) from the Dresden ImageDatabase (Nikon D70), each processed with dcraw bilinear interpolationand proprietary content-adaptive Adobe Lightroom interpolation

I uniform LSB embedding

I exclude covers with >5 % flat blocks (size: 3× 3 )

Matthias Kirchner

Page 24: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 9 /16

Experimental Setup

I 10, 000 BOSSBase grayscale images (size: 512× 512), sampled onto aBayer grid to apply plain bilinear CFA interpolation

I 3, 400 raw color image patches (size: 512× 512) from the Dresden ImageDatabase (Nikon D70), each processed with dcraw bilinear interpolationand proprietary content-adaptive Adobe Lightroom interpolation

I uniform LSB embedding

I exclude covers with >5 % flat blocks (size: 3× 3 )

Matthias Kirchner

Page 25: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 10 /16

Steganalysis Results (I) – Estimation

0 0.1 0.2 0.3 0.4 0.50.001

0.005

0.01

0.05

0.1

4N R4N 4NLS8 LS8

global KB8

plain bilinear interpolation, green channel

embedding rate p

MAE

Matthias Kirchner

Page 26: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 11 /16

Steganalysis Results (II) – Estimation

0 0.1 0.2 0.3 0.4 0.50.001

0.005

0.01

0.05

0.1

4D 2V R4D 2VLS8 LS8 LS8

global KB8

plain bilinear interpolation, red channel

embedding rate p

MAE

Matthias Kirchner

Page 27: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 12 /16

Steganalysis Results (III) – Estimation

0 0.1 0.2 0.3 0.4 0.50.001

0.005

0.01

0.05

0.1

4N R4N 4NLS8 LS8

global KB8

dcraw bilinear interpolation, green channel

embedding rate p

MAE

Matthias Kirchner

Page 28: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 13 /16

Steganalysis Results (IV) – Detection

I increasing mismatch between CFA modelling assumptions and reality letsFKB8 gain advantage

(p = 0.01) bilinear adaptive

plain dcraw LightroomN=7,408 N=3,316 N=3,166

F FP50 EER FP50 EER FP50 EER

Standard WS (KB8)

FKB8 0.35 0.41 0.23 0.33 0.08 0.19FLS8 0.38 0.43 0.26 0.34 0.09 0.20

Proposed CFA-WS (4N)

F4N 0.01 0.05 0.15 0.20 0.19 0.30FLS8 0.01 0.04 0.10 0.16 0.11 0.23 green channel

Matthias Kirchner

Page 29: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 14 /16

Summary and Outlook

I color image steganography is hard

—and largely unexplored

I good news: substantial steganalysis performance boosts hinge onsufficient information about CFA interpolation function

I bad news: image forensics researchI good news: counter-forensics researchI join forces?

I come up with plausible communication channels for grayscale images?

Matthias Kirchner

Page 30: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 14 /16

Summary and Outlook

I color image steganography is hard—and largely unexplored

I good news: substantial steganalysis performance boosts hinge onsufficient information about CFA interpolation function

I bad news: image forensics researchI good news: counter-forensics researchI join forces?

I come up with plausible communication channels for grayscale images?

Matthias Kirchner

Page 31: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 14 /16

Summary and Outlook

I color image steganography is hard—and largely unexplored

I good news: substantial steganalysis performance boosts hinge onsufficient information about CFA interpolation function

I bad news: image forensics researchI good news: counter-forensics researchI join forces?

I come up with plausible communication channels for grayscale images?

Matthias Kirchner

Page 32: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 14 /16

Summary and Outlook

I color image steganography is hard—and largely unexplored

I good news: substantial steganalysis performance boosts hinge onsufficient information about CFA interpolation function

I bad news: image forensics research

I good news: counter-forensics researchI join forces?

I come up with plausible communication channels for grayscale images?

Matthias Kirchner

Page 33: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

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WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 14 /16

Summary and Outlook

I color image steganography is hard—and largely unexplored

I good news: substantial steganalysis performance boosts hinge onsufficient information about CFA interpolation function

I bad news: image forensics researchI good news: counter-forensics research

I join forces?

I come up with plausible communication channels for grayscale images?

Matthias Kirchner

Page 34: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

livin

gkn

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dge

WW

UM

ünst

er

WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 14 /16

Summary and Outlook

I color image steganography is hard—and largely unexplored

I good news: substantial steganalysis performance boosts hinge onsufficient information about CFA interpolation function

I bad news: image forensics researchI good news: counter-forensics researchI join forces?

I come up with plausible communication channels for grayscale images?

Matthias Kirchner

Page 35: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

livin

gkn

owle

dge

WW

UM

ünst

er

WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER Steganalysis in Technicolor 14 /16

Summary and Outlook

I color image steganography is hard—and largely unexplored

I good news: substantial steganalysis performance boosts hinge onsufficient information about CFA interpolation function

I bad news: image forensics researchI good news: counter-forensics researchI join forces?

I come up with plausible communication channels for grayscale images?

Matthias Kirchner

Page 36: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

Plausible Grayscale Images?

Page 37: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

Plausible Grayscale Images?

Page 38: Steganalysis in Technicolor - Binghamton Universityws.binghamton.edu/kirchner/papers/2014_ICASSP_WS-CFA_slides.pdf · e g WWU er WMESTFÄLISCHEILHELMSÜNSTER-UNIVERSITÄT Steganalysis

Steganalysis in Technicolor*Boosting WS Detection of Stego Images from CFA-Interpolated Covers

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*Similarities with existing companies and trademarks are accidental.

WESTFÄLISCHEWILHELMS-UNIVERSITÄTMÜNSTER