A Universal Image Forensics Strategy Based on Steganalytic ...
Transcript of 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
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Outline
•Motivation
•Previous Work
•The proposed universal strategy
•The experimental results and discussions
•Conclusion
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Outline
•Motivation
•Previous Work
•The proposed universal strategy
•The experimental results and discussions
•Conclusion
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Motivation
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Image forgeries are abused
Motivation
• It become easy to modify digital images
– Image editing software are powerful and user-friendly
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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?
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Outline
•Motivation
•Previous work
•The proposed universal strategy
•The experimental results and discussions
•Conclusion
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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.
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Outline
•Background
•Previous work
•The proposed universal strategy
•The experimental results and discussions
•Conclusion
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The proposed strategy
• Universal image manipulations detection.
–Detect various operations
– Identify the type of image manipulations
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The proposed strategy
• 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
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The proposed strategy
• Model the inherent properties within images.
–Different operations usually modify the model in
different manners in the corresponding feature space.
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The proposed strategy
• Some inherent correlations within images.
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The correlations in spatial and frequency domain within a image
The proposed strategy
• Some inherent correlations within images.
• Steganalysis have developed some powerful features
based on such correlations.
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The correlations in spatial and frequency domain within a image
The proposed strategy
• Image steganography can be regarded a specific type
of image manipulation .
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The relationship between image tampering and steganography
The proposed strategy
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Original Image
Median filtering
WOW with 0.4bpp
The proposed strategy
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Original Image
Difference Image
Difference Image
The proposed strategy
• 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.
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The proposed strategy
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The diagram of the proposed strategy
• Some steganalytic features would be promising in
image forensics.
Outline
•Background
•Previous work
•The proposed universal strategy
•The experimental results and discussions
•Conclusion
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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%.
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Experimental results #1
• 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
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Experimental results #1
• 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
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Experimental results #2
• 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%.
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Experimental results #2
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• 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)
Experimental results #2
• 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
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Experimental results #2
• 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
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• Steganalytic features can be regarded as universal
features.
Experimental results #3
• 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
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The asterisk “*” here denotes that the corresponding accuracy is less than 1%
Experimental results #3
• 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
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The asterisk “*” here denotes that the corresponding accuracy is less than 1%
Experimental results #3
• 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
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Experimental results #3
• 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
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• The detection performance of most specific
methods are rather poor for multiple classification.
Outline
•Background
•Previous work
•The proposed universal strategy
•The experimental results and discussion
•Conclusion
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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.
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