Camera model identification with unknown models

Huang, Yonggang, Zhang, Jun and Huang, Heyan 2015, Camera model identification with unknown models, IEEE transactions on information forensics and security, vol. 10, no. 12, pp. 2692-2704, doi: 10.1109/TIFS.2015.2474836.

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Title Camera model identification with unknown models
Author(s) Huang, Yonggang
Zhang, JunORCID iD for Zhang, Jun
Huang, Heyan
Journal name IEEE transactions on information forensics and security
Volume number 10
Issue number 12
Start page 2692
End page 2704
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-12-01
ISSN 1556-6013
Keyword(s) Camera model identification
Unknown models
Machine learning
Digital forensics
Science & Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Summary Feature based camera model identification plays an important role for forensics investigations on images. The conventional feature based identification schemes suffer from the problem of unknown models, that is, some images are captured by the camera models previously unknown to the identification system. To address this problem, we propose a new scheme: Source Camera Identification with Unknown models (SCIU). It has the capability of identifying images of the unknown models as well as distinguishing images of the known models. The new SCIU scheme consists of three stages: 1) unknown detection; 2) unknown expansion; and 3) (K+1)-class classification. Unknown detection applies a k-nearest neighbours method to recognize a few sample images of unknown models from the unlabeled images. Unknown expansion further extends the set of unknown sample images using a self-training strategy. Then, we address a specific (K+1)-class classification, in which the sample images of unknown (1-class) and known models (K-class) are combined to train a classifier. In addition, we develop a parameter optimization method for unknown detection, and investigate the stopping criterion for unknown expansion. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed SCIU scheme. When unknown models present, the identification accuracy of SCIU is significantly better than the four state-of-art methods: 1) multi-class Support Vector Machine (SVM); 2) binary SVM; 3) combined classification framework; and 4) decision boundary carving.
Language eng
DOI 10.1109/TIFS.2015.2474836
Field of Research 080106 Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
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Citation counts: TR Web of Science Citation Count  Cited 10 times in TR Web of Science
Scopus Citation Count Cited 13 times in Scopus
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