Blind Verification of Digital Image Originality:A Statistical Approach

Babak Mahdian, Radim Nedbal, and Stanislav Saic

Sibelius Seraphini

CSI 445 - Digital Image Forensics

Sibelius Seraphini (CSI 445) Digital Image Forensics 1 / 11

Introduction

Trustworthiness of digital images is essential for many areas

forensic investigationcriminal investigationjournalism

One possible approach to check image integrity

extract image featurescompare these features with a reference set

Problem of this approach

reference sets for verification of digital image integritycollected from unknown environments

Sibelius Seraphini (CSI 445) Digital Image Forensics 2 / 11

Introduction

Trustworthiness of digital images is essential for many areas

forensic investigationcriminal investigationjournalism

One possible approach to check image integrity

extract image featurescompare these features with a reference set

Problem of this approach

reference sets for verification of digital image integritycollected from unknown environments

Sibelius Seraphini (CSI 445) Digital Image Forensics 2 / 11

Introduction

Trustworthiness of digital images is essential for many areas

forensic investigationcriminal investigationjournalism

One possible approach to check image integrity

extract image featurescompare these features with a reference set

Problem of this approach

reference sets for verification of digital image integritycollected from unknown environments

Sibelius Seraphini (CSI 445) Digital Image Forensics 2 / 11

Problem addressed in this paper

Given a database consisting of “unguaranteed” images;

How to identify which images are original from the camera and whichhave been modified by software ?

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Related Work

Active Approaches

data hidingdigital signatures

Blind Methods

image splicingcolor filter array interpolationgeometric transformationscloningcomputer graphics generated photosJPEG compression inconsistenciesfingerprint based

Sibelius Seraphini (CSI 445) Digital Image Forensics 4 / 11

Related Work

Active Approaches

data hidingdigital signatures

Blind Methods

image splicingcolor filter array interpolationgeometric transformationscloningcomputer graphics generated photosJPEG compression inconsistenciesfingerprint based

Sibelius Seraphini (CSI 445) Digital Image Forensics 4 / 11

Basic Notation

Digital Images

pixel datametadata

Camera ID vector (−→cm)

makermodel

Fingerprint vector (−→θ )

quantization tablethumbnail

Sibelius Seraphini (CSI 445) Digital Image Forensics 5 / 11

Basic Notation

Digital Images

pixel datametadata

Camera ID vector (−→cm)

makermodel

Fingerprint vector (−→θ )

quantization tablethumbnail

Sibelius Seraphini (CSI 445) Digital Image Forensics 5 / 11

Basic Notation

Digital Images

pixel datametadata

Camera ID vector (−→cm)

makermodel

Fingerprint vector (−→θ )

quantization tablethumbnail

Sibelius Seraphini (CSI 445) Digital Image Forensics 5 / 11

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

Sibelius Seraphini (CSI 445) Digital Image Forensics 6 / 11

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

Sibelius Seraphini (CSI 445) Digital Image Forensics 6 / 11

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

Sibelius Seraphini (CSI 445) Digital Image Forensics 6 / 11

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

Sibelius Seraphini (CSI 445) Digital Image Forensics 7 / 11

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

Sibelius Seraphini (CSI 445) Digital Image Forensics 7 / 11

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

Sibelius Seraphini (CSI 445) Digital Image Forensics 7 / 11

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

Sibelius Seraphini (CSI 445) Digital Image Forensics 7 / 11

Experimental Results

Proposed fingerprints

FMarkers - EXIF MarkersFQTs - luminance and chrominance quantization tablesFThumb - information on the JPEG thumbnail image

Reference Image Data Set

5 million images of FlickrGround-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

Sibelius Seraphini (CSI 445) Digital Image Forensics 8 / 11

Experimental Results

Proposed fingerprints

FMarkers - EXIF MarkersFQTs - luminance and chrominance quantization tablesFThumb - information on the JPEG thumbnail image

Reference Image Data Set

5 million images of FlickrGround-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

Sibelius Seraphini (CSI 445) Digital Image Forensics 8 / 11

Experimental Results

Proposed fingerprints

FMarkers - EXIF MarkersFQTs - luminance and chrominance quantization tablesFThumb - information on the JPEG thumbnail image

Reference Image Data Set

5 million images of FlickrGround-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

Sibelius Seraphini (CSI 445) Digital Image Forensics 8 / 11

Experimental Results

Proposed fingerprints

FMarkers - EXIF MarkersFQTs - luminance and chrominance quantization tablesFThumb - information on the JPEG thumbnail image

Reference Image Data Set

5 million images of FlickrGround-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

Sibelius Seraphini (CSI 445) Digital Image Forensics 8 / 11

Conclusions

image fingerprints are useful to identify the image originality

this paper provides a statistical approach to handle information noisein a “unguaranted” databases of images

positive results in identification of original images

Sibelius Seraphini (CSI 445) Digital Image Forensics 9 / 11

Conclusions

image fingerprints are useful to identify the image originality

this paper provides a statistical approach to handle information noisein a “unguaranted” databases of images

positive results in identification of original images

Sibelius Seraphini (CSI 445) Digital Image Forensics 9 / 11

Conclusions

image fingerprints are useful to identify the image originality

this paper provides a statistical approach to handle information noisein a “unguaranted” databases of images

positive results in identification of original images

Sibelius Seraphini (CSI 445) Digital Image Forensics 9 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was taken

check the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper 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 pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide 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 misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleading

cannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference set

a reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformation

extract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Discussions

strength

can identify with which camera a photo was takencheck the integrity of a database of images instead of just one imageper timeprovide a confidence value of how likely is a image original or modified

weakness

can only be applied to database of users that took the pictureextracting camera ID vector and image fingerprint could be misleadingcannot handle fingerprints that are not in the reference seta reference data set with ground-truth information is needed tovalidate the image originality

improvements

employ another blind verification method to obtain the ground-truthinformationextract the image fingerprint from the pixel data

Sibelius Seraphini (CSI 445) Digital Image Forensics 10 / 11

Questions ?

Sibelius Seraphini (CSI 445) Digital Image Forensics 11 / 11