Abstract
We present in this paper an algorithm that is capable of clustering images taken by an unknown number of unknown digital cameras into groups, such that each contains only images taken by the same source camera. It first extracts a sensor pattern noise (SPN) from each image, which serves as the fingerprint of the camera that has taken the image. The image clustering is performed based on the pairwise correlations between camera fingerprints extracted from images. During this process, each SPN is treated as a random variable and a Markov random field (MRF) approach is employed to iteratively assign a class label to each SPN (i.e., random variable). The clustering process requires no a priori knowledge about the dataset from the user. A concise yet effective cost function is formulated to allow different “neighbors” different voting power in determining the class label of the image in question depending on their similarities. Comparative experiments were carried out on the Dresden image database to demonstrate the advantages of the proposed clustering algorithm.
Highlights
Nowadays, digital imaging devices, especially mobile phones with built-in cameras, have become an essential part of modern life
We presented our preliminary study in [49], where each sensor pattern noise (SPN) is treated as a random variable and Markov random field (MRF) is used to iteratively update the class labels
Based on the similarity matrix, each SPN is treated as a random variable to be assigned a class label, and a reference similarity r is estimated to serve as a rough boundary between the intra- and inter-class similarities in order to encode a cost function using a Markov random field (MRF)
Summary
Digital imaging devices, especially mobile phones with built-in cameras, have become an essential part of modern life. A typical circumstance is that a number of images are collected under proper legal procedures from social networks for forensic analysis, but the devices which have been used to take these images are not available. If those images can be clustered into a number of groups, each including the images acquired by the same camera, the forensic investigators will be able to link the images to particular devices and in a better position to associate
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