Detect digital image forgeries
1 / 55

Detect Digital Image Forgeries - PowerPoint PPT Presentation

  • Uploaded on

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.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Detect Digital Image Forgeries' - dior

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

History of photo manipulation
History of photo manipulation

  • 1860 the portrait of Lincoln is a composite of Lincoln’s head and John Calhoun’s body

History of photo manipulation1
History of photo manipulation

  • 1917: “Cottingley fairies

History of photo manipulation2
History of photo manipulation

  • 1930s: Stalin had disgraced comrades airbrushed out of his pictures

History of photo manipulation3
History of photo manipulation

  • 1936: same story with Mao

History of photo manipulation4
History of photo manipulation

  • 1936: same story with Mao

History of photo manipulation5
History of photo manipulation

  • Oprah Winfrey head on Ann-Margret

History of photo manipulation6
History of photo manipulation

  • 1994: O.J. Simpson’s mug shot modified to appear moremenacing

History of photo manipulation8
History of photo manipulation

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

History of photo manipulation10
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.

Cue in forgeries detection
Cue in Forgeries Detection

  • Light Transport Difference

  • Acquisition Difference

  • Model Detect

Detect inconsistencies in lighting
Detect inconsistencies in Lighting

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

Color model
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

Image intensity model
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

Detect duplicated image region
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.

Forgeries using duplicated image1
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


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

Detect by tracking re sample
Detect by Tracking Re-sample

  • Processing in making forgeries often necessary to resize or rotate.

  • Assume resizing by linear or cubic interpolation method.


  • Resample by factor of 4/3


  • Use EM algorithm to estimate

Rotated and resized
Rotated and Resized

  • Upsampled by 15% and rotated by 5%

  • Rotated by 5% and upsampled by 15%

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.

Pattern noise detection of its presence1

  • Most digital camera with CCD or CMOS use color filter array (CFA)


  • 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

Noise model
Noise Model

  • xij : signal from light

  • ηij: random shot noise

  • cij: dark current

  • εij: read-out noise

Learn pnu
Learn PNU

  • F : denoising filtering

  • Training by more than 50 picture


  • Random select n region with m masks

  • Estimate


  • Luk?, J., J. Fridrich, et al. "Detecting digital image forgeries using sensor pattern noise." Proc. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII 6072: 16?9.

  • Lyu, S. and H. Farid (2005). "How realistic is photorealistic?" IEEE Transactions on Signal Processing 53(2 Part 2): 845-850.

  • Ng, T., S. Chang, et al. (2005). Physics-motivated features for distinguishing photographic images and computer graphics, ACM New York, NY, USA.

  • Popescu, A. and H. Farid "Exposing digital forgeries by detecting duplicated image regions." Department of Computer Science, Dartmouth College.

  • Popescu, A. and H. Farid (2005). "Exposing digital forgeries by detecting traces of resampling." IEEE Transactions on Signal Processing 53(2 Part 2): 758-767.

  • Popescu, A. and H. Farid (2005). "Exposing digital forgeries in color filter array interpolated images." IEEE Transactions on Signal Processing 53(10 Part 2): 3948-3959.