Refining PRNU-based detection of image forgeries

Photo Response Non-Uniformity (PRNU) noise can be considered as a spread-spectrum watermark embedded in every image taken by the source imaging device. It has been effectively used for localizing the forgeries in digital images. The noise residual extracted from the image in question is compared with the reference PRNU in a sliding-window based manner. If their normalized cross correlation, which servers as a decision statistic, is below a pre-determined threshold (e.g., by Neyman-Pearson criterion), the center pixel in the window is declared as forged. However, the decision statistic is calculated over the forged and the non-forged regions when the sliding window falls near the boundary of the two different regions. As a result, the corresponding pixels of the forged region are probably wrongly identified as genuine ones. To alleviate this problem, we analyze the correlation distribution in the problematic region and refine the detection by weighting the decision threshold based on the altered correlation distribution. The effectiveness of the proposed refining algorithm is confirmed through the results of detecting three different kinds of realistic image forgeries.