Capture device identification from digital images using Kullback-Leibler divergence

Abstract

It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to controversy named disputed image and ii) a set of eligible capture devices with which the disputed image could have been shot. In order to define a device statistical fingerprint, a set of reference digital images is produced for each eligible capture device. The device statistical fingerprint is estimated averaging the statistical distribution of the photo response non-uniformity (PRNU) signal extracted from each set of reference digital images. Then, a comparison based on Kullback-Leibler divergence (KLD) is performed between the statistical fingerprint for each capture device and the statistical distribution of the PRNU signal extracted from the disputed image. Considering that KLD is a non-symmetric measure, the capture device, for which the smallest KLD has been estimated, will be chosen such as the one that shot the disputed image. The effectiveness of the proposed method was estimated by using a case study, which includes eight eligible capture devices, each of which shot thirty reference images and twenty disputed images. Then, the performance of the proposed method was like the performance of the methods that use peak-to-correlation energy as the discrimination criterion when they were applied to the case study. Finally, the proposed method offers two advantages; it reduces the processing time when the PRNU signal is extracted from digital image and it avoids the aberration produced by the lens into the PRNU signal.