PRNU registration under scale and rotation transform based on convolutional neural networks

Abstract

Assessing if an image comes from a specific device is fundamental in many application scenarios. The most promising techniques to solve this problem rely on the Photo Response Non Uniformity (PRNU), a unique trace left during image acquisition. A PRNU fingerprint is computed from several images of a given device, then it is compared with the probe residual noise by means of correlation. However, such a comparison requires that PRNUs are synchronized: even small image transformations can spoil this task. Most of the attempts to solve the registration problem rely on time consuming brute-force search, which is prone to missing detections and false positives. In this paper, the problem is addressed from a computer vision perspective, exploiting recent image registration techniques based on deep learning, and focusing on scaling and rotation transformations. Experiments show that the proposed method is both more accurate and faster than state-of-the-art approaches.