The document discusses a statistical approach to verify the originality of digital images by extracting and comparing image features against a reference set. It addresses the challenge of identifying original images from a database of unguaranteed images and highlights experimental results showing the effectiveness of the proposed methods. The findings suggest that image fingerprints can reliably indicate image authenticity, although limitations exist, such as dependency on reference data sets.

1. Blind Verification of Digital Image Originality:
A Statistical Approach
Babak Mahdian, Radim Nedbal, and Stanislav Saic
Sibelius Seraphini
CSI 445 - Digital Image Forensics
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2. Introduction
Trustworthiness of digital images is essential for many areas
forensic investigation
criminal investigation
journalism
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3. Introduction
Trustworthiness of digital images is essential for many areas
forensic investigation
criminal investigation
journalism
One possible approach to check image integrity
extract image features
compare these features with a reference set
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4. Introduction
Trustworthiness of digital images is essential for many areas
forensic investigation
criminal investigation
journalism
One possible approach to check image integrity
extract image features
compare these features with a reference set
Problem of this approach
reference sets for verification of digital image integrity
collected from unknown environments
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5. Problem addressed in this paper
Given a database consisting of “unguaranteed” images;
How to identify which images are original from the camera and which
have been modified by software ?
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6. Related Work
Active Approaches
data hiding
digital signatures
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7. Related Work
Active Approaches
data hiding
digital signatures
Blind Methods
image splicing
color filter array interpolation
geometric transformations
cloning
computer graphics generated photos
JPEG compression inconsistencies
fingerprint based
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8. Basic Notation
Digital Images
pixel data
metadata
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9. Basic Notation
Digital Images
pixel data
metadata
Camera ID vector (−→cm)
maker
model
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10. Basic Notation
Digital Images
pixel data
metadata
Camera ID vector (−→cm)
maker
model
Fingerprint vector (
−→
θ )
quantization table
thumbnail
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11. Reference Data Set - S
S = Cm × Θ × U
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12. Reference Data Set - S
S = Cm × Θ × U
Cm: contains all the camera ID vectors
Θ: contains all possible fingerprints vectors
U: contains all users ID.
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13. Reference Data Set - S
S = Cm × Θ × U
Cm: contains all the camera ID vectors
Θ: contains all possible fingerprints vectors
U: contains all users ID.
−→cm,
−→
θ , u
u has taken the photo
−→cm is the camera used
−→
θ the left fingerprint vector
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14. A Statistical Approach for Noise Removal
“Testing” tuple
t0 = −−→cm0,
−→
θ0
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15. A Statistical Approach for Noise Removal
“Testing” tuple
t0 = −−→cm0,
−→
θ0
Null hypothesis
H0 :
−→
θ0 can’t be a fingerprint of −−→cm0
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16. A Statistical Approach for Noise Removal
“Testing” tuple
t0 = −−→cm0,
−→
θ0
Null hypothesis
H0 :
−→
θ0 can’t be a fingerprint of −−→cm0
Test statistic
T −−→cm0,
−→
θ0 = u| −−→cm0,
−→
θ0, u ∈ S
hypergeometric distribution
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17. A Statistical Approach for Noise Removal
“Testing” tuple
t0 = −−→cm0,
−→
θ0
Null hypothesis
H0 :
−→
θ0 can’t be a fingerprint of −−→cm0
Test statistic
T −−→cm0,
−→
θ0 = u| −−→cm0,
−→
θ0, u ∈ S
hypergeometric distribution
Rejecting H0
if T is too big and greater than a threshold
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18. Experimental Results
Proposed fingerprints
FMarkers - EXIF Markers
FQTs - luminance and chrominance quantization tables
FThumb - information on the JPEG thumbnail image
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19. Experimental Results
Proposed fingerprints
FMarkers - EXIF Markers
FQTs - luminance and chrominance quantization tables
FThumb - information on the JPEG thumbnail image
Reference Image Data Set
5 million images of Flickr
Ground-truth data - 2400 images (24 cameras, 100 digital images each)
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20. Experimental Results
Proposed fingerprints
FMarkers - EXIF Markers
FQTs - luminance and chrominance quantization tables
FThumb - information on the JPEG thumbnail image
Reference Image Data Set
5 million images of Flickr
Ground-truth data - 2400 images (24 cameras, 100 digital images each)
Fingerprints not in the reference image data set have been removed
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21. Experimental Results
Proposed fingerprints
FMarkers - EXIF Markers
FQTs - luminance and chrominance quantization tables
FThumb - information on the JPEG thumbnail image
Reference Image Data Set
5 million images of Flickr
Ground-truth data - 2400 images (24 cameras, 100 digital images each)
Fingerprints not in the reference image data set have been removed
Worked well to check the digital images integrity
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22. Conclusions
image fingerprints are useful to identify the image originality
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23. Conclusions
image fingerprints are useful to identify the image originality
this paper provides a statistical approach to handle information noise
in a “unguaranted” databases of images
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24. Conclusions
image fingerprints are useful to identify the image originality
this paper provides a statistical approach to handle information noise
in a “unguaranted” databases of images
positive results in identification of original images
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25. Discussions
strength
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26. Discussions
strength
can identify with which camera a photo was taken
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27. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
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28. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
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29. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
weakness
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30. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
weakness
can only be applied to database of users that took the picture
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31. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
weakness
can only be applied to database of users that took the picture
extracting camera ID vector and image fingerprint could be misleading
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32. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
weakness
can only be applied to database of users that took the picture
extracting camera ID vector and image fingerprint could be misleading
cannot handle fingerprints that are not in the reference set
Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

33. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
weakness
can only be applied to database of users that took the picture
extracting camera ID vector and image fingerprint could be misleading
cannot handle fingerprints that are not in the reference set
a reference data set with ground-truth information is needed to
validate the image originality
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34. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
weakness
can only be applied to database of users that took the picture
extracting camera ID vector and image fingerprint could be misleading
cannot handle fingerprints that are not in the reference set
a reference data set with ground-truth information is needed to
validate the image originality
improvements
Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

35. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
weakness
can only be applied to database of users that took the picture
extracting camera ID vector and image fingerprint could be misleading
cannot handle fingerprints that are not in the reference set
a reference data set with ground-truth information is needed to
validate the image originality
improvements
employ another blind verification method to obtain the ground-truth
information
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36. Discussions
strength
can identify with which camera a photo was taken
check the integrity of a database of images instead of just one image
per time
provide a confidence value of how likely is a image original or modified
weakness
can only be applied to database of users that took the picture
extracting camera ID vector and image fingerprint could be misleading
cannot handle fingerprints that are not in the reference set
a reference data set with ground-truth information is needed to
validate the image originality
improvements
employ another blind verification method to obtain the ground-truth
information
extract the image fingerprint from the pixel data
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37. Questions ?
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