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Examining Photo Response Non-Uniformity for the Comparison of Cameras. Zeno Geradts PhD / Maarten van der Mark BS / Wiger van Houten MS Partially funded by the European Commission within the project FIDIS www.fidis.net. Overview. Introduction Noise and PRNU sources

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examining photo response non uniformity for the comparison of cameras

Examining Photo Response Non-Uniformity for the Comparison of Cameras

Zeno Geradts PhD / Maarten van der Mark BS / Wiger van Houten MS

Partially funded by the European Commission within the project FIDIS www.fidis.net

Nederlands Forensisch Instituut Laan van Ypenburg 6, 2497 GB Den Haag

overview
Overview
  • Introduction
  • Noise and PRNU sources
  • Comparison of denoising methods
  • Application to YouTube videos
  • Performance
  • Statistics (Bayesian)
  • Conclusions
fidis
FIDIS
  • Network of Excellence within the European Union, with 20 partners ranging from Universities, Privacy protection agencies and companies such as IBM and Microsoft
  • www.fidis.net
introduction
Introduction
  • Each image sensor has a unique `fingerprint’, the PRNU pattern, that is detectable in all images the sensor produces
  • This `fingerprint’ is used to establish the image origin
noise and prnu sources
Noise and PRNU sources
  • Different types of noise:
    • 2 Categories:
      • Random noise
        • Temporally variable
        • Statistical distributions
        • Can be reduced by averaging multiple frames
      • Pattern noise (PRNU and FPN)
        • Not temporally variable but systematic (spatial variable)
  • How do image sensors work?
    • CCD image sensors
    • CMOS Active Pixel Sensors
ccd image sensors
CCD image sensors
  • Basic building block (gate)
  • Silicon substrate
  • Apply positive voltage to the gate
  • Electron-hole pairs generated in depletion region are confined
  • Read out
  • Charge converted to voltage at the sense node
cmos aps image sensors
CMOS APS image sensors
  • Cpd: photodiode capacitor
  • M1: reset transistor
  • M2: source follower
  • M3: row select transistor
  • M4: bias transistor
noise and prnu sources 2
Noise and PRNU sources (2)
  • The characteristic pattern extracted from the images contains 2 systematic contributions:
    • Multiplicative PRNU, depends on illumination
    • Fixed Pattern Noise (FPN), does not depend on illumination
  • Also: common components in the extracted pattern (e.g. CFA interpolation)
noise and prnu sources 3
Noise and PRNU sources (3)
  • FPN (general):
    • Crystal defects in the silicon lattice introduced during growth
    • Impurities
    • The size of the detector/potential well
    • Contamination during fabrication
    • Non-uniform oxide/gate thickness
    • In CMOS: additional sources (each transistor)
noise and prnu sources 4
Noise and PRNU sources (4)
  • PRNU
    • The depth of the detector/potential well
    • Larger active area: more incident photons
    • Non-uniform oxide layer: results in non-uniform potential wells
    • Deeper potential well: more photons absorbed (wavelength dependent)
    • In CMOS: additional sources
image denoising pattern extraction
Image denoising – pattern extraction
  • Denoising algorithms do not discriminate between noise and image details. There is always a tradeoff.
  • PRNU is a nonperiodic discontinuous signal
  • Pattern = Image – F(Image) , F is the denoising filter
  • Correlate the patterns from `questioned’ videos with reference patterns
old method
Old method
  • Gaussian smoothing filter
  • Advantages:
    • Simple
    • Very fast
  • Disadvantages:
    • Distorts edge integrity
    • Image residue left behind in the pattern
new method
New method
  • Wavelet based denoising filter [Lukas et al]*
  • Disadvantages
    • Slower
    • Diadic images only
  • Advantages
    • Preserves edges (edge detection)
    • Spatial adaptive
    • Works really well

*Digital Camera Identification from Sensor Pattern Noise (2005) – Lukas, Fridrich, Goljan

youtube 1
YouTube (1)
  • Accepts large amount of input formats:

Xvid, DivX, WMV, 3GP, …

  • Downloadable as H.264/MPEG-4 AVC with e.g. keepvid.com
  • Maximum resolution (H.264): 480x360
  • Aspect ratio generally does not change
youtube 2
YouTube (2)
  • When the resolution of the video uploaded to YouTube is smaller than 480x360, generally no resolution change occurs (exceptions)
  • When the resolution of the video exceeds 480x360, the resolution is changed to 480x360 or lower (depending on the aspect ratio)

e.g. 640x480  480x360

640x360  480x270

in practice natural video
In practice – natural video
  • Record 30 seconds of natural video, recorded with XVID/WMV
  • Upload this video to YouTube
  • Download the video with keepvid.com
  • Extract individual frames
  • Estimate noise pattern
in practice flatfield video
In practice – flatfield video
  • If natural video downloaded has a resolution lower than 480x360, record the flatfield video (RAW) with the same resolution, use these frames
  • Otherwise (e.g. 640x480), record flatfield video in native resolution, and upload (download) them to (from) YouTube
    • Alternative: resize flatfield video to match the dimension of the YouTube video (e.g. 640x480  480x360)
performance 1
Performance (1)
  • Works really well when natural video is recorded in native resolution, both with XVID and WMV9 (Messenger) with large range of codec settings, even for shorter samples
  • Works reasonably well when aspect ratio has been changed
  • Problematic with very low resolution (Vodafone: 176x144)
  • Does not always work when the video was binned during recording (e.g. native resolution of 640x480, recorded in 320x240x)
performance 2 creative live im webcam
Performance (2) – Creative Live! IM Webcam
  • Problem: need to set two parameters for extracting the patterns
  • These parameters depend on a large amount of variables:
    • Content
    • Compression
    • Codec
    • Resolution
    • etc.
  • Only from empirical data; impossible in casework
comparison creative live im 640x480 native recorded in 352x288
Comparison – Creative Live! IM, 640x480 native, recorded in 352x288
  • Old method (σ=0.6, threshold 5, 4x4 averaging):

New Method (σ_n=4.5, σ_f=4.5):

logitech communicate stx 1
Logitech Communicate STX (1)
  • Logitech : 640x480 (native), XVID q4
  • Logitech : 640x480 (native), variable XVID quality
logitech communicate stx 2
Logitech Communicate STX (2)
  • Logitech : 320x240, XVID q4
  • Logitech : 320x240, variable XVID quality
likelihood ratio
Likelihood ratio

Comparing patterns

Value of the evidence:

likelihood ratio1
Likelihood ratio

In practice:

No overlapping histograms

We do not know the origin of the questioned image

likelihood ratio2
Likelihood ratio

To artificially find the pdf of Hd at Hp we use an estimator

Example:

add-constant estimator

Suppose you look at the

colours of passing cars:

1 blue car, 1 red car, and 1 green car

Q: what is the chance the next car is blue?

A: 1/3? Or smaller?

A: (1+1)/7 for blue; 1/7 for a new colour

likelihood ratio3
Likelihood ratio

More complicated estimators available, but the essence is the same: reserve a probability for unseen species, based upon the data we encountered so far

Simple Good-Turing approach

likelihood ratio4
Likelihood ratio

SGT: problems:

Car colours (green, blue, red, etc) is discrete, but some colours exist that can be seen as both (blue or green?)

We are considering correlation values, continuous data. We have to divide the values into `species’ ourselves

conclusion 1
Conclusion (1)

We understand the origins of the pattern noise that is used to perform device identification

We understand which random noise sources are present

A likelihood ratio can be found, but there are some unanswered questions before this approach can be used

conclusions 2
Conclusions (2)
  • It is possible to identify the source video camera based on the PRNU pattern, even after the video has been uploaded to YouTube
  • The new method, although computationally more intensive, performs much better
conclusions 3
Conclusions (3)
  • Limitations: (very) low resolution videos, changed aspect ratios, ‘subsampled’ recordings
  • No way to find the ideal parameter directly from the video or frames: this is a problem when the abovementioned limitations are met
future research
Future research
  • Using the methods on databases of child pornography to link cameras
  • Improvement of algorithms
  • Validation in casework
questions
Questions ?
  • zeno@holmes.nl