Image Tampering Detection Using Bayesian Analytical Methods

1
Image Tampering Detection Using Bayesian
Analytical Methods

• 04/11/2005
• As presented by Jason Kneier
• ELEN E6886
• Spring 2005

2
The Problem

• Common image processing tools are capable of
  creating forgeries undetectable to the eye
• Data can also be hidden in regions of an image
  where it is less likely to perturb the original
  image

3
The Solution

• Develop a statistical method to detect tampering
  and forgeries of images

4
Proposal

• Use a Bayesian framework to determine
  authenticity of images based on computed feature
  vectors of image statistics

5
Methods

• Feature vectors of interest
• Wavelet decomposition
• Biocoherence

6
System Diagram
Wavelet Decomposition into feature vectors
Bayesian analysis of feature vectors
Input image
Region is authentic
Region has been tampered with
7
Outputs

• Determine locations of suspected tampering, and
  degree of confidence in determination

8
References

• 1 A. C. Popescu and H. Farid, Exposing Digital
  Forgeries by
• Detecting Traces of Re-sampling, IEEE
  Transactions on Signal Processing, 53(2)758-767,
  2005.
• 2 A.C. Popescu and H. Farid, Statistical Tools
  for Digital Forensics, 6th International
  Workshop on Information Hiding, Toronto, Canada,
  2004.
• 3 S. Lyu and H. Farid, How Realistic is
  Photorealistic?, IEEE Transactions on Signal
  Processing, 53(2)845-850, 2005.
• 4 Tian-Tsong Ng, Shih-Fu Chang, Blind
  Detection of Photomontage using Higher Order
  Statisics, Online http//www.ee.columbia.edu/qi
  bin/papers/qibin2004_iscas_1.pdf, Columbia
  University, 2004.
• 5 R. Duda, P. Hart and D. Stork, Pattern
  Classification. New York, John Wiley Sons,
  2001.
• 6 T. Cover and J. Thomas, Elements of
  Information Theory. New York, John Wiley Sons,
  1991.
• 7 W. Pratt, Digital Image Processing. New York,
  John Wiley Sons, 2001.
• 8 A. Papoulis and S. Pillai, Probability,
  Random Variables and Stochastic Processes.
  Boston, McGraw Hill, 2002.