Pooled steganalysis in JPEG: how to deal with the spreading strategy?

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Pooled steganalysis in JPEG:

how to deal with the spreading strategy?

Ahmad ZAKARIA^1,2, Marc CHAUMONT^1,4, G´erard SUBSOL^1,3 LIRMM¹, Univ Montpellier², CNRS³, Univ Nˆımes⁴, Montpellier,

France

December 11, 2019

WIFS’2019, IEEE International Workshop on Information Forensics and Security, December 9-12, 2019, Delft, The Netherlands.

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Introduction

Outline

Introduction

Pooled steganalysis architecture Experimental protocol

Results

Conclusions and perspectives

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Introduction

Steganography / Steganalysis

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Introduction

Batch steganography / Pooled steganalysis

Alice

Embedding

Bob

Bag of cover images

Bag of cover/stego

images

Spreading Strategy

S Message

m

Alice:

I

spreads a message m

∈ {0,

1}

^|m|

,

I

in multiple covers,

I

using a strategy s

∈ S

.

A. D. Ker, “Batch steganography and pooled steganalysis,” in IH’06

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Introduction

Examples of possible spreading strategies

Joint Cover

Cover bag

Classical embedding

algorithm

Greedy strategy [2]

algorithm

Linear strategy [2]

algorithm

The 6 evaluated spreading strategies in this paper,

S

=

{IMS,

DeLS

,

DiLS, Greedy, Linear

,

and Uses

−β}

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Introduction

Pooled steganalysis: how to deal with the spreading strategy?

Alice

Embedding

Bob Bag of cover

images

Spreading Strategy S Message

m

Many possibilities for Alice to spread the message;

What about Eve, the steganalyst?

Recent approaches opt for

pooling

individual scores (more general) Let us denote, f , a Single Image Detector (SID);

For example a payload predictor (quantitative steganalysis):

f :

R^r×c →R⁺

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Introduction

Pooled steganalysis: how to deal with the spreading strategy?

Alice

Embedding

images

m

Many possibilities for Alice to spread the message;

What about Eve, the steganalyst?

Recent approaches opt for

pooling

individual scores (more general)

Let us denote, f , a Single Image Detector (SID);

For example a payload predictor (quantitative steganalysis):

f :

R^r×c →R⁺

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Introduction

Pooled steganalysis: how to deal with the spreading strategy?

Alice

Embedding

images

m

Many possibilities for Alice to spread the message;

What about Eve, the steganalyst?

Recent approaches opt for

pooling

individual scores (more general) Let us denote, f , a Single Image Detector (SID);

For example a payload predictor (quantitative steganalysis):

f :

R^r×c →R⁺

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Introduction

Pooled steganalysis: how to deal with the spreading strategy?

Alice

Embedding

images

m

Many possibilities for Alice to spread the message;

What about Eve, the steganalyst?

Recent approaches opt for

pooling

individual scores

Cover bag Stego Pooling bag

function g

Classification x1

x2

xb-1

x_b SID f(x1) f(x2)

f(xb-1) f(xb) Bag of cover/stego

images

. . . . . .

Bag of scores

. . .

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Introduction

Recent studies

I

[1] Hypothesis: Eve

does not know

the spreading strategy

⇒

best pooling strategy = averaging the individual scores

I

[2] Hypothesis: Eve

does know

the spreading strategy

⇒

knowledge of the strategy = improves steganalysis results.

I

[3] Hypothesis: Eve

does know

the spreading strategy

⇒

knowledge of the strategy = improves steganalysis results.

I [1] R. Cogranne, “A sequential method for online steganalysis,” in WIFS’2015.

I [2] T. Pevn´y and I. Nikolaev, “Optimizing pooling function for pooled steganalysis,” in WIFS’2015.

I [3] R. Cogranne, V. Sedighi, and J. J. Fridrich, “Practical strategies for content-adaptive batch steganography and pooled steganalysis,” in ICASSP’2017.

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Introduction

The addressed question

Cover bag Stego Pooling bag

function g

Classification x1

x2

xb-1

xb SID f(x1) f(x2)

f(xb-1) f(xb) Bag of cover/stego

images

. . . . . .

Bag of scores

. . .

Hypothesis: Eve

does not know

the spreading strategy.

Can Eve “do better” than averaging the individual scores?

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Pooled steganalysis architecture

Outline

Introduction

Pooled steganalysis architecture

Experimental protocol Results

Conclusions and perspectives

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T. Pevny and I. Nikolaev general architecture

x₁

x₂

xb-1

xb

SID f(x1) f(x2)

f(xb-1) f(xb)

IMAGES

Bag of scores

PARZEN window

. . . . . . . . .

SVM

. . .

Cover Stego

Given a vector of SID scores z =

{f

(x

1

), ..., f (x

_b

)}:

h =

"

1 b

X

f(xi)∈z

k (f (x

i

), c

1

), ... , 1 b

X

f(xi)∈z

k (f (x

i

), c

p

)

# ,

with

{c_i}^p_i=1

a set of equally spaced real positive values,

and k(x

,

y) = exp(−γ

||x−

y||

²

)).

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T. Pevny and I. Nikolaev general architecture

x₁

x₂

xb-1

x_b SID f(x1) f(x2)

f(xb-1) f(xb)

IMAGES

Bag of scores

PARZEN window

. . . . . . . . .

SVM

. . .

Cover Stego

I

Histogram

→

can treat a bag of any dimension,

I

Histogram

→

invariant to the sequential order in the bag.

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The Single Image Detector (SID)

I

Note: Alice embeds using J-UNIWARD (512×512 BossBase1.01 QF=75).

I

Quantitative steganalysis in JPEG [1].

I

GFR cleaned and normalized:

I Gabor Features Residuals (GFR) of dimension 17 000 [2],

I Clean cleaned from NaN values and from constant values

→reduced to 16 750,

I Normalize using random conditioning [3].

Learning: 5 000 covers + 5 000 stego per payload size ({0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1} bpc).

I [1] J. Kodovsk´y and J. J. Fridrich, “Quantitative steganalysis using rich models,” in EI’2013 MWSF.

I [2] X. Song, F. Liu, C. Yang, X. Luo, and Y. Zhang, “Steganalysis of adaptive JPEG steganography using 2d gabor filters,” in IH&MMSec’2015.

I [3] M. Boroumand and J. J. Fridrich, “Nonlinear feature normalization in steganalysis,” in IH&MMSec 2017.

I Note: M. Chen, M. Boroumand, and J. J. Fridrich, “Deep learning regressors for quantitative steganalysis,”

in EI’2018 MWSF, is more efficient.

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Experimental protocol

Outline

Introduction

Pooled steganalysis architecture Experimental protocol

Results

Conclusions and perspectives

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Alice: Batch spreading strategies

Alice

Embedding

Bob

Bag of cover images

images

Spreading Strategy

S Message

m

1. Greedy strategy: spreading into as few covers as possible.

2. Linear strategy: spreading evenly.

3. Uses -β strategy: spreading evenly across a fraction of covers.

4. IMS strategy: spreading in an unique artificial image.

5. DeLS strategy: spreading at the same deflection coefficient (MiPod model).

6. DiLS strategy: spreading at the same distortion.

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Eve: Pooling strategies

I

g

clair

: Eve (clairvoyant) knows the spreading strategy.

SVM learned on the known strategy s

∈ S

.

I

g

disc

: Eve (discriminative) does not know the spreading strategy. SVM learned on all the strategies

S.

I

g

_max

: Maximum function AND

τ_max

by minimizing P

_e

over

S.

I

g

mean

: Average function AND

τ_min

by minimizing P

e

over

S.

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Bags for the learning and for the test

g

_clair

(clairvoyant) learning:

I

Choose one bag size b

∈ B

=

{2,

4, 6, 10, 20, 50, 100, 200},

I

Choose one spreading strategies s

∈ S

,

I

Generate 5 000 cover bags and 5 000 stego bags (0.1 bptc).

g

_clair

testing:

I

Choose the same bag size b,

I

Choose the same spreading strategies s ,

I

Generate 5 000 cover bags and 5 000 stego bags (0.1 bptc).

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Bags for the learning and for the test

g

_disc

(discriminative) , g

_max

, and g

_mean

learning:

I

Choose one bag size b

∈ B

=

{2,

4, 6, 10, 20, 50, 100, 200},

I

Choose

all

the spreading strategies from

S,

I

Generate 5 000 cover bags and 5 000 stego bags.

833 bags per strategy (0.1 bptc).

g

_disc

(discriminative) , g

_max

, and g

_mean

testing:

I

Choose the same bag size b,

I

Choose one spreading strategies s

∈ S

(unknown from Eve),

I

Generate 5 000 cover bags and 5 000 stego bags (0.1 bptc).

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Results

Outline

Introduction

Pooled steganalysis architecture Experimental protocol

Results

Conclusions and perspectives

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Results

Alice: Spreading strategies comparison (Eve clairvoyant)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

2 4 6 1 0 2 0 5 0 1 0 0 2 0 0

DeLS DiLS IMS Linear Greedy Uses Beta0.5

g

clair

(Clairvoyant)

Figure: Spreading strategies comparison in theclairvoyantcase (10 runs).

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Results

Eve: Pooling function comparisons

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

2 4 6 1 0 2 0 5 0 1 0 0 2 0 0

g clair g disc g mean g max

Pooling functions

Figure: Pooled steganalysis comparison (10 runs).

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Conclusions and perspectives

Outline

Introduction

Pooled steganalysis architecture Experimental protocol

Results

Conclusions and perspectives

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Conclusions

Up-to-date algorithms:

I

modern embedding (J-Uniward),

I

6 spreading strategies (3 moderns),

I

modern (generic) pooling architecture.

→

Coherent results with past papers.

The take away messages:

I

For Alice: DeLS is a really interesting spreading strategy.

I

For Eve: g

_disc

pooling can improve the detectability

if Eve does not know the spreading strategy.

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To be continued...

Future:

I

DeLS with a DCT model,

I

Robustness to the bag size variation (learn only once with various size),

I

Robustness to the mismatch in the spreading strategy (uses a different strategy in the test; Examples in [1]),

I

Minimize the Pe (for g

disc

) differently for each strategy,

I

Use something more powerful than an SVM,

I

Extend to deep learning,

I

Go toward a simulation of a game (GAN philosophy),

[1] X. Liao, J. Yin, “Two Embedding Strategies for Payload Distribution in Multiple Images Steganography”, in ICASSP’2018.