A fast source-oriented image clustering method for digital forensics

Chang Tsun Li, Xufeng Lin

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
25 Downloads (Pure)


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.
Original languageEnglish
Article number69
Pages (from-to)1-16
Number of pages16
JournalEurasip Journal on Image and Video Processing
Publication statusPublished - Oct 2017


Dive into the research topics of 'A fast source-oriented image clustering method for digital forensics'. Together they form a unique fingerprint.

Cite this