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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Detect Digital Image Forgeries

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Detect digital image forgeries

Detect Digital Image Forgeries

Ting-Wei Hsu


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 manipulation7

History of photo manipulation


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 manipulation9

History of photo manipulation


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.


History of photo manipulation11

History of photo manipulation


History of photo manipulation12

History of photo manipulation

  • March 2004


History of photo manipulation13

History of photo manipulation

  • February 2008:


History of photo manipulation14

History of photo manipulation

  • August 2007


History of photo manipulation15

History of photo manipulation

  • November 2007


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.


Detect inconsistencies in lighting1

Detect inconsistencies in Lighting


Detect inconsistencies in lighting2

Detect inconsistencies in Lighting


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


Image intensity model1

Image Intensity Model


Results

Results


Results1

Results


Using in forgeries detection

Using in Forgeries Detection


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 image

Forgeries Using Duplicated Image


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


Results3

Results


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


Resample1

Resample


Resample2

Resample


Resample3

Resample

  • Use EM algorithm to estimate


Resized estimate

Resized Estimate


Rotated estimate

Rotated Estimate


Rotated and resized

Rotated and Resized

  • Upsampled by 15% and rotated by 5%

  • Rotated by 5% and upsampled by 15%


Forgery detect

Forgery Detect


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.


Image fetch processing

Image Fetch Processing


Pattern noise detection of its presence1

PATTERN NOISE & DETECTION OF ITS PRESENCE

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


Detect digital image forgeries

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


Forgery detection mask

Forgery Detection Mask


Forgery detection

Forgery Detection


Forgery detection1

Forgery Detection


Forgery detection2

Forgery Detection


Forgery detection3

Forgery Detection


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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