shenzhen university image steganalysis: challenges · 2017. 8. 5. · acknowledgement members in my...

Jiwu Huang

Shenzhen University,China

BUCHAREST·2017

Image Steganalysis:

Challenges

Shenzhen University

Acknowledgement

Members in my team

Dr. Weiqi Luo and Dr. Fangjun Huang

Sun Yat-sen Univ., China

Dr. Bin Li and Dr. Shunquan Tan, Mr. Jishen

Zeng

Shenzhen Univ., China

Shenzhen University

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Shenzhen UniversityOutlines

Steganography in Images

Steganalysis vs. Steganography

Challenges in Steganalysis

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Shenzhen UniversitySteganography

What steganography?

undetectable

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Steganography vs. Cryptography?

@2*$#&(*%7*= ?Ek(M)

encryptionplaintext

ciphertext

plaintext

Cryptography

Unreadable !

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Steganography and Cryptography?

Hidingplaintext stego

plaintext

MM

Nothing !

Steganography

cover

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Encryption

Secret message

Encrypted bits

Unsecure

channelHiding

Cover image

Security：

1) difficulty to find stego images

2) difficulty to extract the hidden bits

Steganography

Cryptography

Steganography

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Security for steganography

Secure steganography: undetectable

Can only be estimated with a probability

about random guessing

“Not necessary or sufficient to be

imperceptible”

“Imperceptible” does not means

“undetectable”

“perceptible” does not means

“detectable”

Steganography

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K-L distance: security (Cachin 2004)

security

relative entropy

PX---distribution of covers

PY---distribution of stegos

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Maximum Mean Discrepancy-based

Security （Fridrich 2008）

What function f should be selected?What function f should be selected?

xi , yi: Sample of PX, PY

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Steganalyzer’s ROC-based Security

(Memon 2003)

ROC:TP rate ~FP rate

security:

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Shenzhen UniversitySteganalysis vs. Steganography

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Shenzhen UniversitySteganalysis vs. Steganography

Steganalysis

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Shenzhen University

Early targeted approaches

Attacking LSB-based steganography

Attacking OutGuess, MB, F5, YASS

Advanced universal approaches

Image Quality Features (Memon et. al)

Calibration Based Features (Fridrich et. al)

Moment Based Features (Farid et. al)

Correlation Based Features (Moulin, Sullivan, Shi)


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“Cat & Mouse Game”


LSB-based

(J-steg)

Model-based

(MB1)

Compensation-

based (MB2)

Histogram-based

(Chi-square), RS

Over fitting

Block artifact due to

many changes

steganalysissteganography

targeted approaches

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“Cat & Mouse Game”


matrix coding

wet paper code

STC(syndrome-trellis codes)

Distortion functions

High-dimensional

features

+machine learning

Adaptive+ Adaptive+

universal approaches

steganalysissteganography

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Y: feature vector

Classifier: mapping Y→i

i: i＝0，nature image; i＝1，stego image

Y i

testing

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training

Classifiers: SVM, Fisher linear discriminant,

neural network, ensemble, others.

nature

imagesstego

images

Steganalysis wins steganography

Statistical features

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Statistical features

nature

images

stego

images

steganography wins Steganalysis

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Counterwork between

steganography and steganalysis

statistical modelof nature images

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Statistical model of nature images

Is there such universal statistical model

It seem to be difficult to answer

If yes, how to model?

At least, it is not easy to model texture

regions


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How to find the traces a steganographic

scheme?

Limited performance in the existing

features

Objective image quality measures,

Calibration-based features, statistical

moments, gray-level co-occurrence matrix

based features, Markov process based

features, SPAM (subtractive pixel adjacency

matrix), SRM (spatial rich model)

SRM: generating features (Fridrich 2012)


Computing residuals using different

submodels

Ri,j = X’i,j(Ni,j)-cXi,j

Truncation and quantization by

quantization step q and threshold T

Ri,j = truncT(round(Ri,j/q))

Using co-occurrence matrices for feature

extraction

SRM

Fridrich et. al, “Rich Models for Steganalysis of Digital Images,”

IEEE T-IFS, 7(3): 868-882, 2012 24

Performance of SRM under different datasets

SRM + ensemble classifier

Image Dataset: downsampling from

high resolution raw images

Downsampling type: bicubic, bilinear, lanczos2, nearest

5000 images for training, 5000 for testing.

stegnographic algorithm:

S-UNIWARD, 0.4bpp

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Shenzhen University

How to find the traces a steganographic

scheme?

How to design efficient features?

To achieve better performance

Methodology for constructing features

Compact features

Low computation load


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Mis-matchng between training and

testing databases

Mis-matching issue

Mis-matching: datasets for training and

testing have different properties

Steganalyzers should be robust

In steganalysis community: down-

sampling leaves traces


Downsampling effect on steganalysis

SRM + ensemble classifier

Image Dataset: downsampling from

high resolution raw images

5000 images for training, 5000 for testing.

stegnographic algorithm: S-UNIWARD, 0.4bpp 29

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Does deep learning work?

Great success in pattern recognition

Does DL work in recognizing

“Imperceptible” differences?

Works on some easy forensics issues

However, how about steganalysis?

Have not seen good progress in

steganalysis in spatial domain

Recent progress in steganalysis for JPEG

domain


Method Accuracy

DCTR features (8000dims) 0.65

PHARM features (12600dims) 0.68

Proposed deep network 0.75

Deep learning in JPEG steganalysis

JPEG steganography: J-Uniward, 0.4bpp

Training: 800K images from ImageNet

Testing: 200K images from ImageNet

Zeng et,al, “Large-scale JPEG image steganalysis using hybrid

deep-learning framework,” Submitted to IEEE Trans. on IFS 31

JPEG stegnographic algorithm: J-UNIWARD, 0.4bpp

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Dataset for training

Golden rules in machine learning

data samples should be at least 20-50

times with the total number of

parameters.

Steganalysis in spatial domain: High-

dimensional features vs. small dataset

Taking SRM as an example


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SRM: very high feature dimension vs.

small training dataset

features：34671

Image database: 10 K, training images: 5K

JPEG stegnographic algorithm: Juniward, 0.4bpp

Fridrich et. al, “Rich Models for Steganalysis of Digital Images,”

IEEE T-IFS, 7(3): 868-882, 2012

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Shenzhen University

DNN: very large number of network parameters vs.

small training dataset

parameters： 100K

Image database: 10 K, training images: 5K

JPEG stegnographic algorithm: Juniward, 0.4bpp

Xu et., al, “Structural Design of Convolutional Neural Networks for Steganalysis,”

IEEE SPL, 23(5), 708-712, 2016.

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Dataset for training

Uncompressed images are not commonly

used in practice

Very huge manpower to collect an

applicable raw dataset


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Stegnography is a good way in secret

communication and thus steganalysis is

of important

Have some progress in laboratory

There are still many challenges

Deep learning may be useful in JPEG

steganalysis

Conclusions

shenzhen university image steganalysis: challenges · 2017. 8. 5. · acknowledgement members in my...

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