Exposing Digital Image Forgeries by Using Canonical Correlation Analysis

Abstract

In this paper, we propose a new method to detect the forgeries in digital images by using photo-response non-uniformity (PRNU) noise features. The method utilizes canonical correlation analysis (CCA) to measure linear correlation relationship between two sets of PRNU noise estimation from images taken by the same camera. The linear correlation relationship maximizes the correlation between the noise reference pattern(or PRNU noise estimation) and PRNU noise features from the same camera. To further improve the detection accuracy rate, the difference of variance between an image region and its smoothed version is used to categorize the image region into heavily textured region class or non-heavily textured region class. For a heavily textured region or a non-heavily textured region, Neyman-Pearson decision is used to calculate the corresponding threshold, and get the final result of detection.