Common source identification of images in large databases

Photo-response non-uniformity noise patterns are a robust way to identify the source of an image. However, identifying a common source of images in a large database may be impractical due to long computation times. In this paper a solution for large volume digital camera identification is proposed, which combines, and sometimes slightly modifies, existing methods for a 500 times improvement in the speed of common source identification. Single image comparisons are often plagued by considerable noise contamination from scene content and random noise, which makes it harder to accomplish reliable common source identification. Therefore a new method is introduced that can increase true positive rates by more than 45% at very low computation costs. Analysis of real data from a fraud case shows the effectiveness of the proposed method. As a whole the proposed solution makes it possible to analyze a large database in forensically relevant time, without resorting to large and expensive computer clusters.