A Theoretical Framework for Estimating False Acceptance Rate of PRNU-Based Camera Identification

Abstract

In recent years, camera identification methods have attracted attention in the field of digital forensics. The existing camera identification methods use features, such as the Exif header data and image noise, that indicate the characteristics of the camera. Of them, photo-response non-uniformity (PRNU) noise contains the unique features of an image sensor and is different for each individual camera. A camera identification method using the PRNU noise should have high identification ability, and a camera identification method using the pairwise magnitude relations of the clustered PRNU noise was previously proposed. In general, identification accuracy is estimated from test data sets, such as the Dresden image database. However, identification accuracy can be evaluated only with respect to the range of images within a database in the conventional evaluation method. A more detailed accuracy evaluation method is required for practical use. Furthermore, studies have not yet reported a false acceptance rate (FAR) evaluation method for the clustered PRNU pair-based camera identification capable of guaranteeing a low FAR (e.g., $\rm {FAR}=10^{-9}$ ). In this paper, we proposed a new pixel clustering method that guarantees An FAR for camera identification using pairs of clustered PRNU noise, and evaluate its FAR based on a probability calculation of a mathematical model. In addition, we investigate the appropriate cluster size by using the Shapiro–Wilk test for an FAR evaluation. We show that it is possible to reliably calculate the FAR of a clustered PRNU noise pair-based camera identification method by using the proposed evaluation method. To demonstrate the validity of our calculations, we compare the actual identification result with the result of the proposed calculation. In this case, we used 16 958 query images from the Dresden image database, which is a benchmark data set. The results of our evaluation indicate that this identification method maintains a false rejection rate of less than 5% (10%) for 5 (8) of the 10 tested cameras even for FAR $ = 10^{-9}$ .