Fast image clustering of unknown source images

Succeeding in determining information about the origin of a digital image is a basic issue of multimedia forensics. In particular it could be interesting to individuate which is the specific camera (brand and/or model) that has taken that photo; to do that, additional knowledge are needed about the camera such as its fingerprint, usually computed by resorting at the extraction of the PRNU (Photo-Response-Uniformity-Noise) by using a group of images coming from that camera. It is easy to understand that in many application scenarios information at disposal are very limited; this is the case when, given a set of N images, we want to establish if they belong to M different cameras where M is less or, at most, equal to N, without having any knowledge about the source cameras. In this paper a new technique which aims at blindly clustering a given set of N digital images is presented. Such a technique is based on a pre-existing one [1] and improves it both in terms of error probability and of computational efficiency. The system is able, in an unsupervised and fast manner, to group photos without any initial information about their membership. Sensor pattern noise is extracted by each image as reference and the successive classification is performed by means of a hierarchical clustering procedure. Experimental results have been carried out to verify theoretical expectations and to witness the improvements with respect to the other technique. Tests have also been done in different operative circumstances (e.g. asymmetric distribution of the images within each cluster), obtaining satisfactory results.