Estimate of PRNU Noise Based on Different Noise Models for Source Camera Identification

Introduction

Digital forensics science emerged in the last decade in response to the escalation of crimes committed by the use of electronic devices as an instrument used to commit a crime or as a repository of evidences related to a crime (e.g., piracy and child-pornography). For instance a digital camera could be the instrument used to commit a crime and/or a digital photograph, being the evidence related to an illegal action, might have been altered to mislead the judgment. One important element of digital forensics is the credibility of the digital evidence in order to assess digital data origin and authenticity. In this paper digital images are taken in account focusing on evaluating image origin determining the specific digital camera which has acquired that content. It is possible to split the source identification problem in two fields (Lanh, 2007): the first is devoted to determine the specific digital camera or scanner and also identify the model and brand that acquired an image (Bayram, 2005; Chen, 2008; Lukas, 2006; Swaminathan, 2008; Sorell, 2009), the second one is dedicated to investigate the kind of device (Caldelli, 2009a; Caldelli, 2009b; Khanna, 2008) that has generated the image under examination (digital camera, scanner, computer graphics images). Various solutions have been proposed in literature to solve the source identification problem analyzing the digital device acquisition process in order to find a fingerprint left by the device like the use of Color Filter Array (CFA) characteristics (Swaminathan, 2008; Bayram, 2005) and the Photo Response Non-Uniformity (PRNU) noise (Caldelli, 2009b; Khanna, 2008; Chen, 2008; Lukas, 2006). The PRNU noise is induced by intrinsic in-homogeneities over the silicon wafer and imperfections generated during sensor manufacturing process of CCD/CMOSs. The PRNU is used as sensor fingerprint and it is commonly employed to solve the problem of digital camera sensor identification. Such a technique is investigated in this paper. The extraction of PRNU noise happens through a digital filtering operation from a set of digital images taken by a camera. After that, the PRNU noise of the to-be-checked image is extracted and compared with the available fingerprints and then the image is classified as taken (or not) by a certain camera. It is important to point out, for the further discussion, that the PRNU noise is deterministically embedded in each image the sensor acquired.