Camera identification by grouping images from database, based on shared noise patterns
This incremental work addresses forensic analysis for law enforcement or security by enabling quick scanning of large image databases to link images from the same camera.
The paper tackles the problem of identifying cameras by grouping images from a database based on shared PRNU noise patterns, achieving faster processing through optimized filters and reduced calculation costs.
Previous research showed that camera specific noise patterns, so-called PRNU-patterns, are extracted from images and related images could be found. In this particular research the focus is on grouping images from a database, based on a shared noise pattern as an identification method for cameras. Using the method as described in this article, groups of images, created using the same camera, could be linked from a large database of images. Using MATLAB programming, relevant image noise patterns are extracted from images much quicker than common methods by the use of faster noise extraction filters and improvements to reduce the calculation costs. Relating noise patterns, with a correlation above a certain threshold value, can quickly be matched. Hereby, from a database of images, groups of relating images could be linked and the method could be used to scan a large number of images for suspect noise patterns.