A Universal Image Forensics Strategy Based on Steganalytic ...

A Universal Image Forensics Strategy

Based on

Steganalytic Model

Xiaoqing Qiu, Haodong Li, Weiqi Luo, Jiwu Huang

Sun Yat-sen Univ., P.R. China

June 13, 2014 1Sun Yat-sen University

Outline

•Motivation

•Previous Work

•The proposed universal strategy

•The experimental results and discussions

•Conclusion


Outline

•Motivation

•Previous Work



•Conclusion


Motivation


Image forgeries are abused

Motivation

• It become easy to modify digital images

– Image editing software are powerful and user-friendly


Motivation

• It become easy to modify digital images

– Image editing software are powerful and user-friendly

• Increasing image forgeries in our daily life have

raised several forensic questions

– Is a given image authentic or has it been manipulated?

– What image processing has it been done previously?


Outline

•Motivation

•Previous work



•Conclusion


Previous Work

• Many forensic methods have been proposed.

– Exposing splicing images, copy-paste images.

– Detecting image processing operations, such as JPEG compression/recompression, blurring, re-sampling and so on.

• Limitations of the existing works

– Most methods are specific.

– Performance are still far from satisfactory.

– Fail to cope with various types of image manipulations.


Outline

•Background

•Previous work



•Conclusion


The proposed strategy

• Universal image manipulations detection.

–Detect various operations

– Identify the type of image manipulations



• Universal image manipulations detection.

–Detect various operations

– Identify the type of image manipulations

• A universal forensic method should

–Concentrate on the common artifacts left by various

operations

–Focus on some inherent properties which are inevitably

destroyed



• Model the inherent properties within images.

–Different operations usually modify the model in

different manners in the corresponding feature space.



• Some inherent correlations within images.


The correlations in spatial and frequency domain within a image


• Some inherent correlations within images.

• Steganalysis have developed some powerful features

based on such correlations.


The correlations in spatial and frequency domain within a image


• Image steganography can be regarded a specific type

of image manipulation .


The relationship between image tampering and steganography



Original Image

Median filtering

WOW with 0.4bpp



Original Image

Difference Image

Difference Image


• Image manipulation would destroy the inherent

correlations more significantly than steganography.

Advanced steganography Image manipulation

Modified regionsMainly modify the textural

regions

Both textural and smooth

regions

Modification

magnitude±1 Wide range

Modification rateLess than 9%

(e.g. WOW with 0.4bpp)

JPEG compression - 36.55%

Gamma correction - 48.71%

Median filtering - 35.84%

Gaussian blurring - 37.93%

Visual artifacts No obvious visual artifacts

Image content would be

changed for some operations,

e.g. splicing, copy-paste.




The diagram of the proposed strategy

• Some steganalytic features would be promising in

image forensics.

Outline

•Background

•Previous work



•Conclusion


Experimental results #1

• Experimental setup for image splicing detection

–1050 authentic images and 1050 splicing images from the 1st IEEE IFS-TC image forensic challenge.

–Steganalysis feature set:

SRM(Fridrich&Kodovsky TIFS 11) LBP(Shi et al. IH 12)

SPAM(Pevny et al. TIFS 10) CF*(Kodovsky et al. TIFS 12)

CC-Chen(Chen et al. ISCAS 08) CC-PEV(Pevny&Fridrich et al. SPIE 07)

–Specific feature set:

MKF(He et al. PR 12) NIM(Shi et al. ACM MM&Sec 07)

–Ensemble classifier. Training rate – 50%.



• Accuracies for image splicing detection

Proposed Specific methods

Spatial steganalysis JPEG steganalysis

SRM LBP SPAM CF*CC-

Chen

CC-

PEVMKF NIM

QF=75 88.37 86.94 81.05 86.12 82.74 78.70 86.83 73.54

QF=80 89.35 88.47 82.60 87.23 83.96 80.69 86.82 74.25

QF=85 91.22 90.57 83.92 90.19 84.96 81.67 87.98 77.05

QF=90 92.92 92.89 86.50 90.98 86.58 82.74 90.47 79.73

QF=95 94.76 94.36 88.52 92.16 90.50 86.94 92.84 85.32

No compression 97.70 95.31 91.35 91.93 89.91 88.79 93.34 90.45



• Accuracies for image splicing detection

Proposed Specific methods

Spatial steganalysis JPEG steganalysis

SRM LBP SPAM CF*CC-

Chen

CC-

PEVMKF NIM

QF=75 88.37 86.94 81.05 86.12 82.74 78.70 86.83 73.54

QF=80 89.35 88.47 82.60 87.23 83.96 80.69 86.82 74.25

QF=85 91.22 90.57 83.92 90.19 84.96 81.67 87.98 77.05

QF=90 92.92 92.89 86.50 90.98 86.58 82.74 90.47 79.73

QF=95 94.76 94.36 88.52 92.16 90.50 86.94 92.84 85.32

No compression 97.70 95.31 91.35 91.93 89.91 88.79 93.34 90.45



• Experimental setup for processing operations detection

–5 types of image processing operations.

• Gaussian blurring, Gamma correction, JPEG compression, Median filtering, Re-sampling.

–Specific feature set:

AR(Kang et al. APSIPA ASC 12) CE(Stamm&Liu ICIP 08)

JPA(Luo et al. TIFS 10) PPI(Mahdian&Saic TIFS 08)

–Steganalysis feature set:

SRM(Fridrich&Kodovsky TIFS 11) LBP(Shi et al. IH 12)

–Ensemble classifier. Training rate – 50%.




• Parameters of different types of image processing

operations.

Operations Parameters

Gaussian blurring order: 3, 5, 7, 9; var:1.0, 2.0

Gamma correction gamma: 0.5, 0.6, 0.7 0.8 0.9

JPEG compression quality factor: 75, 76, 77, …, 95

Median filtering order: 3, 5, 7, 9

Re-sampling

up sampling: 1, 3, 5, 10, 20, 30, …, 90 (%)

down sampling: 1, 3, 5, 10,15,20, …, 45 (%)

rotation: 1, 3, 5, 10,15,20, …, 45 (degrees)


• Accuracies for image processing operations detection

Feature

set

Gaussian

blurring

Gamma

correction

JPEG

compression

Median

filtering

Re-

sampling

Specific

Methods

AR 98.17 53.11 65.26 97.86 77.56

CE 69.41 96.31 60.41 82.57 54.55

JPA 89.80 50.82 99.18 82.69 65.10

PPI 52.79 50.28 82.91 52.49 86.39

Proposed

SRM 99.98 96.09 99.55 99.75 98.90

LBP 99.90 83.64 99.87 99.81 97.91



• Accuracies for image processing operations detection

Feature

set

Gaussian

blurring

Gamma

correction

JPEG

compression

Median

filtering

Re-

sampling

Specific

Methods

AR 98.17 53.11 65.26 97.86 77.56

CE 69.41 96.31 60.41 82.57 54.55

JPA 89.80 50.82 99.18 82.69 65.10

PPI 52.79 50.28 82.91 52.49 86.39

Proposed

SRM 99.98 96.09 99.55 99.75 98.90

LBP 99.90 83.64 99.87 99.81 97.91


• Steganalytic features can be regarded as universal

features.


• Identifying various types of manipulations with SRM

Predicted

Actual

OriginalGaussian

blurring

Gamma

correction

JPEG

compression

Median

filtering

Re-

samplingSplicing

Original 96.28 * 1.94 * * * 1.24

Gaussian

blurring* 99.54 * * * * *

Gamma

correction 5.37 * 93.19 * * * *

JPEG

compression* * * 99.03 * * *

Median

filtering* * * * 98.76 * *

Re-sampling * * * * * 97.49 *

Splicing 2.69 * * 2.57 * * 93.96


The asterisk “*” here denotes that the corresponding accuracy is less than 1%


• Identifying various types of manipulations with LBP

Predicted

Actual

OriginalGaussian

blurring

Gamma

correction

JPEG

compression

Median

filtering

Re-

samplingSplicing

Original 86.11 * 11.47 * * 1.16 1.24

Gaussian

blurring* 99.54 * * * * *

Gamma

correction 20.03 * 78.29 * * * *

JPEG

compression* * * 99.58 * * *

Median

filtering* * * * 99.26 * *

Re-sampling 1.01 * * * * 96.68 *

Splicing 3.03 * 2.08 6.16 * * 87.46


The asterisk “*” here denotes that the corresponding accuracy is less than 1%


• Average accuracies for identifying various types of

manipulations.

Specific methods Proposed

Feature set AR CE JPA PPI MKF SRM LBP

Accuracy 53.60 37.91 38.74 20.68 90.83 96.89 92.42



• Average accuracies for identifying various types of

manipulations.

Specific methods Proposed

Feature set AR CE JPA PPI MKF SRM LBP

Accuracy 53.60 37.91 38.74 20.68 90.83 96.89 92.42


• The detection performance of most specific

methods are rather poor for multiple classification.

Outline

•Background

•Previous work


•The experimental results and discussion

•Conclusion


Conclusion

• Contributions: Build a bridge between two different research fields, i.e.

image forensics and steganalysis

Steganalytic features can be used as universal features

for detecting various image tampering operations

The proposed strategy outperforms those state-of-the-art

specific forensic methods

• Future work: More image operations and universal stegananlytic

features will be included.

Detecting mixture of image manipulations.


Xiaoqing Qiu

([email protected])


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