Image Sharpening for Efficient Source Camera Identification Based on Sensor Pattern Noise Estimation

Abstract

Sensor pattern noise (SPN) has been widely used for image authentication and camera source identification. Its abundance in terms of the information that it carries along a wide frequency range allows for reliable identification in the presence of many imaging sensors. SPN estimation relies on the difference between a set of images and their smoothened versions to capture the characteristics of the sensor. Therefore, this process uses a part of the sensor noise content which is concentrated in the high frequency range and present in edges, contours and textured areas of the images. In this report, we propose to use a sharpening method to amplify the PRNU components for better estimation, thus enhancing the performance of camera source identification (CSI). Significant improvements have been achieved by the proposed method as demonstrated with two recent source camera identification techniques.