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 Input image Wavelet Decomposition into feature vectors Bayesian analysis of feature vectors 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/~qibin/papers/qibin2004_iscas_1.pdf, Columbia University, 2004. http://www.ee.columbia.edu/~qibin/papers/qibin2004_iscas_1.pdf [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.