Camera Model Identification With Unknown Models

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 s...

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Bibliographic Details
Published in:IEEE transactions on information forensics and security 2015-12, Vol.10 (12), p.2692-2704
Main Authors: Huang, Yonggang, Zhang, Jun, Huang, Heyan
Format: Article
Language:English
Subjects:
Accuracy
Camera model identification
Cameras
Classification
digital forensics
Feature based
Feature extraction
Forensic engineering
Image detection
machine learning
Nickel
Object recognition
Support vector machines
Testing
Training
unknown models
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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.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2015.2474836