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 D igital 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 CFA Interpolation channel color images – CFA 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