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

    Matthias Kirchner and Rainer BhmeUniversity of Mnster

    IEEE ICASSP | Florence, Italy |May 8, 2014

    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

  • livin

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

  • livin

    gkn

    owle

    dge

    WW

    UM

    nst

    er

    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

  • livin

    gkn

    owle

    dge

    WW

    UM

    nst

    er

    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

  • livin

    gkn

    owle

    dge

    WW

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 3 /16

    WS Steganalysis [Fridrich & Goljan, 2004; Ker & Bhme, 2008]I estimates the embedding rate p of uniform LSB replacement embedding in

    grayscale images

    p =2n

    ni=1

    (1) x(p)i

    (x(p)i x

    (0)i

    ) x (0) cover object Znx (p) stego object Znx (0) cover estimatep embedding rate

    w vector of weights

    I cover estimate: linear prediction from spatial neighboorhood

    FKB8 : 14

    12

    14

    12 0

    12

    1412

    14

    FLS8 :b a ba 0 ab a b

    I enhanced variants for various assumptions about cover source andembedding strategies

    Matthias Kirchner

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 3 /16

    WS Steganalysis [Fridrich & Goljan, 2004; Ker & Bhme, 2008]I estimates the embedding rate p of uniform LSB replacement embedding in

    grayscale images

    p =2n

    ni=1

    (1) x(p)i

    (x(p)i x

    (0)i

    ) x (0) cover object Znx (p) stego object Znx (0) cover estimatep embedding rate

    w vector of weights

    I cover estimate: linear prediction from spatial neighboorhood

    FKB8 : 14

    12

    14

    12 0

    12

    1412

    14

    FLS8 :b a ba 0 ab a b

    I enhanced variants for various assumptions about cover source andembedding strategies

    Matthias Kirchner

  • livin

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 3 /16

    WS Steganalysis [Fridrich & Goljan, 2004; Ker & Bhme, 2008]I estimates the embedding rate p of uniform LSB replacement embedding in

    grayscale images

    p =2n

    ni=1

    wi (1) x(p)i

    (x(p)i x

    (0)i

    ) x (0) cover object Znx (p) stego object Znx (0) cover estimatep embedding ratew vector of weights

    I cover estimate: linear prediction from spatial neighboorhood

    FKB8 : 14

    12

    14

    12 0

    12

    1412

    14

    FLS8 :b a ba 0 ab a b

    I enhanced variants for various assumptions about cover source andembedding strategies

    Matthias Kirchner

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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 14 014 1

    14

    0 14 0

    Fred :

    14

    12

    14

    12 1

    12

    14

    12

    14

    bilinear interpolation:

    Matthias Kirchner

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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 14 014 1

    14

    0 14 0

    Fred :

    14

    12

    14

    12 1

    12

    14

    12

    14

    bilinear interpolation:

    Matthias Kirchner

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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 14 014 1

    14

    0 14 0

    Fred :

    14

    12

    14

    12 1

    12

    14

    12

    14

    bilinear interpolation:

    Matthias Kirchner

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 6 /16

    Spatial Neighborhood PredictabilityI OLS linear prediction from all 3 3 neighborhoods (FLS8 ) per categoryI 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: 512512)

    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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 6 /16

    Spatial Neighborhood PredictabilityI OLS linear prediction from all 3 3 neighborhoods (FLS8 ) per categoryI 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: 512512)

    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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 6 /16

    Spatial Neighborhood PredictabilityI OLS linear prediction from all 3 3 neighborhoods (FLS8 ) per categoryI 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: 512512)

    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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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 14 014 0

    14

    0 14 0

    F4D14 0

    14

    0 0 014 0

    14

    F2H

    0 0 012 0

    12

    0 0 0

    F2V

    0 12 00 0 00 12 0

    bilinear interpolation:

    Matthias Kirchner

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 8 /16

    CFA-WS Steganalysis

    I utilization of CFA neighborhood relations in individual color channels

    pC =2|{C}|

    {iC}

    (1) x(p)i

    (x(p)i FC

    (x (p))i

    ) x (p) stego object ZnFC predictor for category

    C {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 p is equivalent toassigning weights

    Matthias Kirchner

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 8 /16

    CFA-WS Steganalysis

    I utilization of CFA neighborhood relations in individual color channels

    pC =2|{C}|

    {iC}

    wi (1) x(p)i

    (x(p)i FC

    (x (p))i

    ) x (p) stego object ZnFC predictor for category

    C {R, 4N, 4D, 2H, 2V}p embedding ratew vector of weights

    green channel red channel

    R4N

    4D

    2H

    2V

    I aggregation to a combinedestimate p is equivalent toassigning weights

    Matthias Kirchner

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

  • livin

    gkn

    owle

    dge

    WW

    UM

    nst

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

  • livin

    gkn

    owle

    dge

    WW

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER 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

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 14 /16

    Summary and Outlook

    I color image steganography is hardand 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

  • livin

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    nst

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 14 /16

    Summary and Outlook

    I color image steganography is hardand 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

  • livin

    gkn

    owle

    dge

    WW

    UM

    nst

    er

    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 14 /16

    Summary and Outlook

    I color image steganography is hardand 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

  • livin

    gkn

    owle

    dge

    WW

    UM

    nst

    er

    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 14 /16

    Summary and Outlook

    I color image steganography is hardand 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

  • livin

    gkn

    owle

    dge

    WW

    UM

    nst

    er

    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 14 /16

    Summary and Outlook

    I color image steganography is hardand 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

  • livin

    gkn

    owle

    dge

    WW

    UM

    nst

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    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER Steganalysis in Technicolor 14 /16

    Summary and Outlook

    I color image steganography is hardand 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

  • Plausible Grayscale Images?

  • Plausible Grayscale Images?

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

    Thanks for your attention!

    *Similarities with existing companies and trademarks are accidental.

    WESTFLISCHEWILHELMS-UNIVERSITTMNSTER