Learning Deep Features for Source Color Laser Printer Identification based on Cascaded Learning

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Color laser printers have fast printing speed and high resolution, and forgeries using color laser printers can cause significant harm to society. A source printer identification technique can be employed as a countermeasure to those forgeries. This paper presents a color laser printer identification method based on cascaded learning of deep neural networks. First, the refiner network is trained by adversarial training to refine the synthetic dataset for halftone color decomposition. Then, the halftone color decomposing ConvNet is trained with the refined dataset. The trained knowledge about halftone color decomposition is transferred to the printer identifying ConvNet to enhance the identification accuracy. Training of the printer identifying ConvNet is carried out with real halftone images printed from candidate source printers. The robustness about rotation and scaling is considered in training process, which is not considered in existing methods. Experiments are performed on eight color laser printers, and the performance is compared with several existing methods. The experimental results clearly show that the proposed method outperforms existing source color laser printer identification methods. (C) 2019 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER
Issue Date
2019-11
Language
English
Article Type
Article
Citation

NEUROCOMPUTING, v.365, no.1, pp.219 - 228

ISSN
0925-2312
DOI
10.1016/j.neucom.2019.07.084
URI
http://hdl.handle.net/10203/267633
Appears in Collection
CS-Journal Papers(저널논문)
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