Practical applications of digital forensics are often faced with the challenge of grouping large-scale suspicious images into a vast number of clusters, each containing images taken by the same camera. This task can be approached by resorting to the use of sensor pattern noise (SPN), which serves as the fingerprint of the camera. The challenges of large-scale image clustering come from the sheer volume of the image set and the high dimensionality of each image. The difficulties can be further aggravated when the number of classes (i.e., the number of cameras) is much higher than the average size of class (i.e., the number of images acquired by each camera). We refer to this as the NC SC problem, which is not uncommon in many practical scenarios. In this paper, we propose a novel clustering framework that is capable of addressing the NC SC problem without a training process. The proposed clustering framework was evaluated on the Dresden image database and compared with the state-of-the-art SPN-based image clustering algorithms. Experimental results show that the proposed clustering framework is much faster than the state-of-the-art algorithms while maintaining a high level of clustering quality.

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