Overview of State-of-the-Art in Digital Image Forensics

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Overview of State-of-the-Art in Digital Image
ForensicsImage Source Identification

• Author H. T. SENCAR and N. MEMON
• Reporter Yao Ge

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Motivation 1/2

• In todays digital age, the creation and
  manipulation of digital images is made simple by
  low-cost hardware and software tools.
• As a result, we are rapidly reaching a situation
  where one can no longer take the authenticity and
  integrity of digital images for granted (and
  more) .

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Motivation 2/2

• The Image Forensics can help us to evaluate the
  reality and integrity of a given digital image
• Eg. evidence
• One of the most popular research directions is
  Image Source Identification (ISI)

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What is Image Source Identification (ISI)?

• Purpose
• To identify the characteristics of digital data
  acquisition device (e.g. Digital camera)
• Aspects
• Source mode Identification
• Individual Source Identification

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Image Acquisition Pipeline 1/3
Lens
Filter(s)
Sensor
Camera Processing
Color Filter Array
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Image Acquisition Pipeline 2/3

• Lens system
• composed of a lens and the mechanisms to control
  exposure, focusing, and image stabilization to
  collect and control the light from the scene.
• Filters
• includes the infra-red and anti-aliasing filters
  to ensure maximum visible quality.
• Image sensor
• An image sensor is an array of rows and columns
  of photodiode elements, or pixels. When light
  strikes the pixel array, each pixel generates an
  analog signal proportional to the intensity of
  light, which is then converted to digital signal
  and processed by the DIP.
• CFA
• Since the sensor pixels are not sensitive to
  color, to produce a color image, a color filter
  array (CFA) is used in front of the sensor so
  that each pixel records the light intensity for a
  single color only.

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Image Acquisition Pipeline 3/3

• DIP
• The output from the sensor with a Bayer (RGB)
  filter (assume) is a mosaic of red, green and
  blue pixels of different intensities. Each pixel
  contains the information of only one color. The
  digital image processor implements interpolation
  (demosaicing) algorithms to recover the missing
  information of the other two colors for each
  pixel.
• The DIP also performs further processing such as
  white balancing, noise reduction, matrix
  manipulation, image sharpening, aperture
  correction, and gamma correction to produce a
  good quality image.

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Source Model Identification

• Image Features
• CFA and Demosaicing Artifacts
• Lens Distortions

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Image Features 1/4

• Principle
• Features extracting Classifiers (criterion)

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Image Features 2/4

• E.g.
• Defines a set of 34 features inspired by
  universal steganalysis techniques
• Features Color features, wavelet coefficient
  statistics, image quality metrics

M. Kharrazi, H. T. Sencar, and N. Memon, Blind
Source Camera Identi?cation, Proc. of IEEE ICIP
(2004)
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Image Features 3/4

• Experimental Results
• 2 cameras case
• (average accuracy98.73)
• 5 cameras case
• (average accuracy88.02)
  

M. Kharrazi, H. T. Sencar, and N. Memon, Blind
Source Camera Identi?cation, Proc. of IEEE ICIP
(2004)
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Image Features 4/4

• Weakness
• an overall decision. What specific features
  contribute much during the process are still
  unknown
• this method may not give a satisfactory result
  with the increasing of the number of cameras
• Conclusion
• this approach is more suitable as a
  pre-processing technique to cluster images taken
  by cameras with similar components and processing
  algorithms

M. Kharrazi, H. T. Sencar, and N. Memon, Blind
Source Camera Identi?cation, Proc. of IEEE ICIP
(2004)
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CFA and Demosaicing Artifacts 1/7

• Choice of CFA
• Demosaicing

Bayer Pattern (RGB)
CMYK
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CFA and Demosaicing Artifacts 2/7

• Principle
• As the interpolation algorithms differ from each
  other in different camera models.
• E.g.
• The author uses the outputs of Expectation/Maximiz
  ation (EM) algorithm as features to detect
  different interpolation.

S. Bayram, H. T. Sencar and N. Memon, Source
Camera Identification Based on CFA Interpolation,
Proc. of IEEE ICIP (2005)
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CFA and Demosaicing Artifacts 3/7

• Expectation/Maximization (EM) Algorithm
• EM algorithm is originally used to detect whether
  a signal has been re-sampled or not.
• It generates two outputs
• The probability map. The value of each point on
  the probability map indicates the probability
  that the point is correlated with its neighbors.
• The estimate of the weighting coefficients which
  represent the amount of contribution from each
  pixel in the interpolation kernel.

S. Bayram, H. T. Sencar and N. Memon, Source
Camera Identification Based on CFA Interpolation,
Proc. of IEEE ICIP (2005)
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CFA and Demosaicing Artifacts 4/7

• several examples of the periodic patterns that
  emerged due to re-sampling

S. Bayram, H. T. Sencar and N. Memon, Source
Camera Identification Based on CFA Interpolation,
Proc. of IEEE ICIP (2005)
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CFA and Demosaicing Artifacts 5/7

• Frequency spectrum of probability maps obtained
  by three types of digital cameras

S. Bayram, H. T. Sencar and N. Memon, Source
Camera Identification Based on CFA Interpolation,
Proc. of IEEE ICIP (2005)
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CFA and Demosaicing Artifacts 6/7

• Experimental results
• The set of weighting coefficients obtained from
  an image, and the peak location and magnitudes in
  frequency spectrum are used as features. An SVM
  classifier is used.

S. Bayram, H. T. Sencar and N. Memon, Source
Camera Identification Based on CFA Interpolation,
Proc. of IEEE ICIP (2005)
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CFA and Demosaicing Artifacts 7/7
S. Bayram, H. T. Sencar and N. Memon, Source
Camera Identification Based on CFA Interpolation,
Proc. of IEEE ICIP (2005)
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Lens Distortions
Radial distortion
Rectified Image

• Radial distortion is due to the change in the
  image magnification with increasing distance from
  the optical axis
• Compensation for radial distortion induces unique
  artifacts in the images
• Choi et al. introduces a second order radial
  symmetric distortion model
• Model parameters are used as classification
  features
• Accuracy 91

K. S. Choi, E. Y. Lam and K. K. Y. Wong, Source
Camera Identification Using Footprints from Lens
Aberration, Proc. of SPIE (2006).
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Conclusion

• Even the initial experiment results of previous
  methods are encouraging, some technique
  limitation still exist in different real
  situation.
• There is a long distance to achieve the
  application level.

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Thank you!

• Dec. 2008