computer vision forensics research powerpoint skidmore summer 2011
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DigitalImageSourceIdenti2icationSkidmoreCollegeFaculty/StudentResearchProject
Summer2011
AdamSteinberger'12MichaelEckmann,AssistantProfessor,
MathematicsandComputerScienceDepartment
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Abstract
Howcanwe
iden+fywherephotoscome
from?Source: buyingguideonline.com
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DigitalForensics
Source: trigonit.com
Weextracta7ributesin
digitalimagestodetermine
sourcecamera.
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ClassifyingCameras
Wecreatedaclassifierthatcreatesafingerprintforeachcamera.
A7ributescomefrom
pixelcolorsindigitalimages.
Sources: jeanierhoades.com,mpc.edu, idfpr.com
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ColorFilterArrays
DigitalcamerastakephotosusingaColorFilter
Array(CFA).
CFAismatrixof+ny
sensorsinsidecameras
thatcaptureonecolorperpixel. Source: en.wikipedia.org
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Demosaicing
Demosaicingprocessescomputethe2missingcolorsforeachpixelfromsome
neighborhoodaroundit.
Typically,camerasuseuniqueproprietary
demosaicingprocesses.
Smoothvsedgepixelsareusually
processeddifferently.Skidmore College 6
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SoftwareDevelopment
Idevelopedoriginalso*waredesigned
tocomputea7ributesthat
relatetoacamera's
demosaicingprocesses.
Source: computerhistory.orgSkidmore College 7
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206 202 197
204 200 196
201 195 194
200
195194 196 204197 206202201
199
199- = 1
MEDIAN
NEIGHBOR
ORIGIN
PIXEL ERROR
SmoothErrorCalculations
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105 204 205
100 200 197
96 198 194
200
194 197 205204198
198 = 2
MEDIAN
NEIGHBOR
ORIGIN
PIXEL ERROR
EdgeErrorCalculations
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Pixelerrorstakenforred,greenandbluecolorsusing3x3,5x5andxpixelwindowsoveren+reimage.
Sta+s+csgeneratedfromerrorsinclude:average,standarddevia+on,skewness,kurtosis,
energyandentropy.
StatisticsfromErrors
Source: allpsych.com
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AttributeSets
6stats:mean,sd,skew,kurt,energy,entropy3neighborhoods:3x3,5x5,x
3colors:red,green,blue
2pixeltypes:smooth,edge
18otherunrelateda;ributes6x3x3x2+18=126a5ributesperimage
Ourdatabasehasover5,500imagesfrom25cameras.
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Ouroriginalclassifierusedsmootherrorsa7ributes,andhada34.96%accuracyrate.
Weaddeda7ributesforedgeerrors,andouraccuracyrateroseto36.2%.
Theclassifierresultsfortheabovearefrom25cameras.Byrandomchance:1/25is4%accuracy.Ourresultsaremuchhigher!
Aclassifierwitha7ributesfromjust3iPhoneshadanaccuracyrateof9.1%.Theseresultsaremuchhigherthanthefirsttwoclassifiersresults.
Results
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Wewanttoincorporateoura7ributesalongwithourcolleaguesa7ributesetforimprovedaccuracy.
Also,weplantoextractthetopN(say5)camera
choicesforeachtestimagewithhighaccuracy.
Wewillimplementcommondemosaicingprocessesthatcamerasusetocomputemorea7ributesets.
FutureWork
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QUESTIONS?
Forfurtherinforma+on,ques+onsorconcerns,pleasecheckoutmywebsite:
amsteinberger.com
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