Exposing Digital Forgeries in Color Filter Array Interpolated Images

By Alin C. Popescu and Hany Farid

Presenting - Anat Kaspi

The Goal Low cost high resolution digital camera, sophisticated

photo editing Digital media can be manipulated very easily

Fake images… Photos no longer hold the unique stature as a definitive recording of events

Automatically detecting digital forgeries in any portion of an image

In contrast to other approaches: watermark, signature Drawback: must be inserted at time of recording

The Technique Digital forgeries may leave no visual clues but they may

alter the underlying statistics of an image

Color image consists of three channels containing samples from different bands of the color spectrum

Most digital cameras are equipped with only a single color sensor and use Color Filter Array (CFA)

The other two missing colors must be estimated from the neighboring to obtain three channel color images – CFA InterpolationCFA Interpolation

The Technique (Cont.) A subset of samples, within a color channel, are

correlated to neighboring samples The correlations are periodic since the color filters

arranged in a periodic pattern

Presence or lack of correlation produced by CFA interpolation can be used to detect forgery

There are many CFA Interpolation algorithms

Bilinear and Bicubic, Median Filter, Gradient Based, Adaptive Color Plane and more…

Example Bilinear interpolation

The Estimated samples are perfectly correlated to their neighbors

The Method - EM Algorithm Two step iterative algorithm We have two models: M1, M2

Outputs: Probability Map – detect if a color image is a result of CFA

interpolation Linear coefficients – used to distinguish between different

CFA interpolation

Results CFA interpolation of their creation Each color channel was independently blurred with 3x3

binomial filter Down sample by factor of two in each direction Re sampled onto Bayer array and CFA interpolated

Collected 100 images: 50 of resolution 512x512, 50 of resolution 1024x1024

Gradient

3x3 median

No CFA interpolation

Results Detecting Localized Tampering Composite images – splicing the non CFA image and the

same CFA interpolated image Plausible forgery created using Adobe Photoshop

Sensitivity and Robustness Tested the sensitivity of the model to

typical distortions that may conceal trace of tampering JPEG compression, additive white

Gaussian noise, Gamma correction

Robustness Measure of similarity between

probability maps of each color channel vs. synthetically generated probability maps

Results: bilinear, bicubic, smooth hue, variable number of gradient - 100%, Median 99%, ACP 97%

Discussion

Advantages The technique works in the absence of any digital

watermark or signature Simple linear model to capture the correlation produced

by CFA interpolation Shown efficacy

Drawbacks Can be attacked by resampleing onto CFA and then

reinterpolating - requires knowledge of camera CFA pattern