Large-scale image clustering based on camera fingerprints

Xufeng Lin, Chang-Tsun Li

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)793-808
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Issue number4
Early online dateDec 2016
Publication statusPublished - Apr 2017


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