Detect digital image forgeries
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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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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


Results2
Results

  • 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
Resample

  • Resample by factor of 4/3




Resample3
Resample

  • 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
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
PATTERN NOISE & DETECTION OF ITS PRESENCE

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


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


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


Detect
Detect

  • Random select n region with m masks

  • Estimate







Reference
Reference

  • 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.


Reference1
Reference

  • http://www.cs.dartmouth.edu/farid/research/digitaltampering/

  • http://www.newseum.org/berlinwall/commissar_vanishes/vanishes.htm

  • http://www.cs.unc.edu/~lazebnik/research/fall08/


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