Unsupervised classification of digital images using enhanced sensor pattern noise

We present in this work an unsupervised image classifier, which is capable of clustering images taken by an unknown number of unknown digital cameras into a number of classes, each corresponding to one camera. The classification system first extracts and enhances a sensor pattern noise (SPN) from each image, which serves as the fingerprint of the camera that has taken the image. Secondly, it applies an unsupervised classifier trainer to a small training set of randomly selected SPNs to cluster the SPNs into classes and uses the centroids of those identified classes as the trained classifier. The classifier trainer treats each SPN as a random variable and uses Markov random field (MRF) approach to iteratively assigns a class label to each SPN (i.e., random variable) based on the class labels assigned to the members of a small set of SPNs, called membership committee, and the similarity values between it and the members of the membership committee until a stop criteria is met. The classifier trainer requires no a priori knowledge about the dataset from the user. Finally the image not included in the small training set are classified using the trained classifier depending on the similarity between their SPNs and the centroids of the trained classifier.