detect digital image forgeries

Detect Digital Image Detect Digital Image ForgeriesForgeries

Detect Digital Image Detect Digital Image ForgeriesForgeries

Ting-Wei HsuTing-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

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

History of photo manipulation

• 1936: same story with Mao

History of photo manipulation

• 1936: same story with Mao

History of photo manipulation

• Oprah Winfrey head on Ann-Margret

History of photo manipulation

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

History of photo manipulation

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 manipulation

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 manipulation

History of photo manipulation

• March 2004

History of photo manipulation

• February 2008:

History of photo manipulation

• August 2007

History of photo manipulation

• November 2007

Cue in Forgeries Detection

• Light Transport Difference

• Acquisition Difference

• Model Detect

Detect inconsistencies in Lighting

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

Detect inconsistencies in Lighting

Detect inconsistencies in Lighting

Color Model• Assumption:

– the surface of interest is Lambertian– the surface has a constant reflectance va

lue– the surface is illuminated by a point light

source infinitely far away

Image Intensity Model

• R : constant reflectance value• N(x,y) : 3 vector representing the surf

ace normal at (x ,y)• A : constant ambient light• L : surface normal

Image Intensity Model

Results

Results

Using in Forgeries Detection

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

• Applying PCA on small fixed size image block.– Reduce dimension representation– This representation is robust to minor va

riations in the image due to additive noise or lossy compression

• Do lexicographic sorting

Results• Take 10 seconds in 512*512 image usi

ng 3 GHz processor

Results

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 by factor of 4/3

Resample

Resample

Resample

• Use EM algorithm to estimate

Resized Estimate

Rotated Estimate

Rotated and Resized

• Upsampled by 15% and rotated by 5%

• Rotated by 5% and upsampled by 15%

Forgery Detect

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

Camera lense Anti-aliasing filter CFA Color interpolate

Color correction and white balence

Gamma correction and kernel filtering

Save file (JPEG compression)

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 n

ature images.• PNU – pixel non-uniformity : different s

ensitivity of pixel to light• Caused by stochastic inhomogenities p

resent in silicon wafer

Noise Model

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

Learn PNU

• F : denoising filtering

• Training by more than 50 picture

Detect• Random select n region with m

masks

• Estimate

Forgery Detection Mask

Forgery Detection

Forgery Detection

Forgery Detection

Forgery Detection

Reference• Luk?, J., J. Fridrich, et al. "Detecting digital image forgeries using sensor pattern nois

e." 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.

Reference• http://www.cs.dartmouth.edu/farid/r

esearch/digitaltampering/• http://www.newseum.org/berlinwall/

commissar_vanishes/vanishes.htm• http://www.cs.unc.edu/~lazebnik/res

earch/fall08/