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Detect Digital Image Forgeries

Detect Digital Image Forgeries. Ting-Wei Hsu. History of photo manipulation. 1860 the portrait of Lincoln is a composite of Lincoln ’ s head and John Calhoun ’ s body. History of photo manipulation. 1917: “ Cottingley fairies. History of photo manipulation.

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Detect Digital Image Forgeries

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  1. Detect Digital Image Forgeries Ting-Wei Hsu

  2. History of photo manipulation • 1860 the portrait of Lincoln is a composite of Lincoln’s head and John Calhoun’s body

  3. History of photo manipulation • 1917: “Cottingley fairies

  4. History of photo manipulation • 1930s: Stalin had disgraced comrades airbrushed out of his pictures

  5. History of photo manipulation • 1936: same story with Mao

  6. History of photo manipulation • 1936: same story with Mao

  7. History of photo manipulation • Oprah Winfrey head on Ann-Margret

  8. History of photo manipulation • 1994: O.J. Simpson’s mug shot modified to appear moremenacing

  9. History of photo manipulation

  10. History of photo manipulation • April 2003: This digital composite of a British soldier in Basra, gesturing to Iraqi civilians urging them to seek cover,

  11. History of photo manipulation

  12. History of photo manipulation • February 2004: Senator John Kerry and Jane Fonda sharing a stage at an anti-war rally emerged during the 2004 Presidential primaries as Senator Kerry was campaigning for the Democratic nomination.

  13. History of photo manipulation

  14. History of photo manipulation • March 2004

  15. History of photo manipulation • February 2008:

  16. History of photo manipulation • August 2007

  17. History of photo manipulation • November 2007

  18. Cue in Forgeries Detection • Light Transport Difference • Acquisition Difference • Model Detect

  19. Detect inconsistencies in Lighting • If the photo was composited, it’s often difficult to match the lighting conditions from individual photographs.

  20. Detect inconsistencies in Lighting

  21. Detect inconsistencies in Lighting

  22. Color Model • Assumption: • the surface of interest is Lambertian • the surface has a constant reflectance value • the surface is illuminated by a point light source infinitely far away

  23. Image Intensity Model • R : constant reflectance value • N(x,y) : 3 vector representing the surface normal at (x ,y) • A : constant ambient light • L : surface normal

  24. Image Intensity Model

  25. Results

  26. Results

  27. Using in Forgeries Detection

  28. Detect Duplicated Image Region • A common manipulation in tampering with an image is to copy and paste portions of the image to conceal a person or object in the scene.

  29. Forgeries Using Duplicated Image

  30. Forgeries Using Duplicated Image • Applying PCA on small fixed size image block. • Reduce dimension representation • This representation is robust to minor variations in the image due to additive noise or lossy compression • Do lexicographic sorting

  31. Results • Take 10 seconds in 512*512 image using 3 GHz processor

  32. Results

  33. Detect by Tracking Re-sample • Processing in making forgeries often necessary to resize or rotate. • Assume resizing by linear or cubic interpolation method.

  34. Resample • Resample by factor of 4/3

  35. Resample

  36. Resample

  37. Resample • Use EM algorithm to estimate

  38. Resized Estimate

  39. Rotated Estimate

  40. Rotated and Resized • Upsampled by 15% and rotated by 5% • Rotated by 5% and upsampled by 15%

  41. Forgery Detect

  42. PATTERN NOISE & DETECTION OF ITS PRESENCE • Detection of digitally manipulated images based on the sensor pattern noise . • Detection whether image take from same camera or from another region.

  43. Image Fetch Processing

  44. PATTERN NOISE & DETECTION OF ITS PRESENCE • Most digital camera with CCD or CMOS use color filter array (CFA)

  45. PRNU • Photo-response non-uniformity noise • Dominate part of the pattern noise in nature images. • PNU – pixel non-uniformity : different sensitivity of pixel to light • Caused by stochastic inhomogenities present in silicon wafer

  46. Noise Model • xij : signal from light • ηij: random shot noise • cij: dark current • εij: read-out noise

  47. Learn PNU • F : denoising filtering • Training by more than 50 picture

  48. Detect • Random select n region with m masks • Estimate

  49. Forgery Detection Mask

  50. Forgery Detection

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